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AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks

by   Yonggan Fu, et al.

The compression of Generative Adversarial Networks (GANs) has lately drawn attention, due to the increasing demand for deploying GANs into mobile devices for numerous applications such as image translation, enhancement and editing. However, compared to the substantial efforts to compressing other deep models, the research on compressing GANs (usually the generators) remains at its infancy stage. Existing GAN compression algorithms are limited to handling specific GAN architectures and losses. Inspired by the recent success of AutoML in deep compression, we introduce AutoML to GAN compression and develop an AutoGAN-Distiller (AGD) framework. Starting with a specifically designed efficient search space, AGD performs an end-to-end discovery for new efficient generators, given the target computational resource constraints. The search is guided by the original GAN model via knowledge distillation, therefore fulfilling the compression. AGD is fully automatic, standalone (i.e., needing no trained discriminators), and generically applicable to various GAN models. We evaluate AGD in two representative GAN tasks: image translation and super resolution. Without bells and whistles, AGD yields remarkably lightweight yet more competitive compressed models, that largely outperform existing alternatives.


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Code Repositories


[ECCV 2020] "All-in-One GAN Compression by Unified Optimization" by Haotao Wang, Shupeng Gui, Haichuan Yang, Ji Liu, and Zhangyang Wang

view repo

1 Introduction

Generative adversarial networks (GANs) (Goodfellow et al., 2014; Zhu et al., 2017) nowadays have empowered many real applications such as image stylization, image editing and enhancement. Those applications have driven the growing demand to deploy GANs, usually their trained generators, on resource-constrained platforms, e.g., for real-time style transfer and super resolution (Shi et al., 2016)

in mobile APPs. However, just like other deep models, GAN generators require heavy memory and computation resources to run, which challenge most mobile devices. For example, the renowned image-to-image translation network, CycleGAN 

(Zhu et al., 2017), would cost 54 GFLOPs to process one 256256 image. For most mobile phones, that latency would notably degrade user experience, if ever feasible.

To reduce this gap, it is a natural motive to refer to model compression techniques (Han et al., 2015). The current mainstream of model compression focuses on deep image classification or segmentation, which, as demonstrated in (Shu et al., 2019), cannot be easily extended to compress GANs (by default, defined as compressing their trained generators), That is mainly due to the vulnerability of their learned mappings (i.e., between high-dimensional structured image spaces), and the notoriously training instability (that challenges the re-training practice in compression). To our best knowledge, at the time of submission, (Shu et al., 2019) is the only published algorithm in GAN compression. They specifically focus on GANs with the cycle-consistency loss, and simultaneously compress generators of both directions. Despite the promising performance, their algorithm cannot be straightforwardly extended to other popular GAN types, such as encoder-decoder GANs (Wang et al., 2018a; Chen et al., 2018b) which are widely used in image editing/enhancement. Moreover, the proposed co-evolution algorithm was built on pruning only, while many other compression techniques, such as quantization (Jacob et al., 2018), knowledge distillation (Polino et al., 2018), and even AutoML (He et al., 2018)

, were left unexplored. Besides, their algorithm’s loss function relied on the original trained discriminator to be available. Yet in practice, this might not always be realistic: as most applications only use trained generators, the discriminators might often have been discarded or no longer available after training.

This paper aims to significantly push forward the application frontier of GAN compression. For this new problem, we extensively refer to the state-of-the-art compression techniques including AutoML and knowledge distillation, while striving for more general techniques that can compress any common GAN generator. The resulting framework, dubbed AutoGAN-Distiller (AGD), is the first AutoML framework dedicated to GAN compression (alongside the concurrent work (Li et al., 2020)), and is also among a few earliest works that explore AutoML for GANs (Gong et al., 2019). AGD is established on a specifically designed search space of efficient generator building blocks, leveraging knowledge from state-of-the-art GANs for different tasks. It then performs differentiable neural architecture search under the target compression ratio (computational resource constraint), which preserves the original GAN generation quality via the guidance of knowledge distillation (Polino et al., 2018). AGD makes no assumption towards GAN structures, loss forms, nor even the availability of trained discriminators. It is therefore broadly applicable to compressing both CycleGANs and other GANs without the cycle loss. We demonstrate AGD on two representative mobile-based GAN applications: unpaired image translation (using a CycleGAN), and super resolution (using an encoder-decoder GAN). In both tasks, AGD overwhelms the state-of-the-arts (Li et al., 2016; Shu et al., 2019; Ledig et al., 2017; Kim et al., 2016).

2 Related Work

2.1 AutoML: Neural Architecture Search

As one of the most significant sub-fields of AutoML (Hutter et al., 2019), Neural Architecture Search (NAS) (Zoph and Le, 2016)

seeks an optimal neural network architecture from data, instead of using hand-crafting. Already by now, NAS methods have outperformed manually designed architectures on a range of tasks such as image classification 

(Tan and Le, 2019; Tan et al., 2019; Howard et al., 2019; Liu et al., 2018a), and segmentation (Chen et al., 2018a; Liu et al., 2019; Chen et al., 2019b). A comprehensive survey of NAS could be found in (Elsken et al., 2018)

Introducing NAS to GANs appears to more challenging due to the training instability and lack of direct performance measures. (Gong et al., 2019)

developed the first NAS framework for GANs, on the task of unconditional image generation from random noise. It adopted a multi-level search strategy with an RNN controller to search for the generator architecture using reinforcement learning. However, the existing NAS for GAN framework is

not directly applicable in our case due to the following reasons:

  • The framework in (Gong et al., 2019) searched for a GAN from scratch, without referring to a trained model. There is also no computational resource constraint in their search. It is hence not a “compression” technique and lacks the ability to distill knowledge from those already-trained, high-performance GANs.

  • (Gong et al., 2019) only demonstrated to find relatively small GAN models that synthesize low-resolution images from noise (e.g., a 6-layer generator on 32 32 CIFAR-10 images), and also did not consider image-to-image GANs. In comparison, the desired GAN compression shall apparently be able to handle the heavily-parameterized GANs on high-resolution images, with image-to-image applications (stylization, editing, enhancement, etc) as the primary focuses.

2.2 Model Compression

With the unprecedented demand for deploying deep models on resource-constrained platforms, many compression methods are proposed to narrow the gap between the model complexity and on-device resources (Han et al., 2020; Wang et al., 2018b; Chen et al., 2020a). Popular techniques include knowledge distillation (Polino et al., 2018), pruning (Han et al., 2015) and quantization (Jacob et al., 2018).

Knowledge distillation was proposed in (Hinton et al., 2015) to transfer the knowledge from one model to another by imitating the soft labels generated by the former. It was widely applied in learning a compact network by fitting the soft labels generated by a pretrained large model (Lopez-Paz et al., 2015; Bulò et al., 2016; Wang et al., 2018c; Chen et al., 2019a, 2020b), therefore compressing the latter.

Pruning (Han et al., 2015) refers to sparsifying the model weights by thresholding the unimportant weights. All of them follow a similar workflow: (iteratively) pruning the model to a smaller size and then retraining to recover accuracy. Structured pruning, e.g. channel pruning (Wen et al., 2016; He et al., 2017), was widely adopted as a hardware-friendly compression means (Gui et al., 2016). (Luo et al., 2017) also suggested to prune filters based on the statistics from the next layer to evaluate a filter’s importance.

Quantization reduces the float-number representations of weights and activations to lower numerical precision  (Wu et al., 2016; Han et al., 2015). The extreme case could even just use binary values (Courbariaux et al., 2015; Rastegari et al., 2016)

. Beyond scalar quantization, vector quantization was widely adopted too in model compression for parameter sharing 

(Gong et al., 2014; Wu et al., 2018).

Recently, (He et al., 2018; Liu et al., 2018b) pointed out that conventional model compression techniques rely on domain experts to explore the large design space trading off, which could be sub-optimal and tedious. The idea of AutoML for model compression has been extended by a number of works, e.g, (Wu et al., 2019; Cheng et al., 2018; Tan and Le, 2019; Tan et al., 2019; Cheng et al., 2018)

, although most of them focused on compressing deep classifiers.

2.3 GANs and GAN Compression

GANs have witnessed prevailing success in many practical applications built on image-to-image translation. CycleGAN (Zhu et al., 2017) and its many successors pioneered in demonstrating GAN’s power in image-to-image translation without paired training data. They explored cycle-consistency loss to avoid mode collapse, enabling both diverse and semantically consistent synthesis, therefore making them popular choices in style transfer-type applications. However, GANs with a cycle loss are often costly and difficult to train. Recent works have found that encoder-decoder GANs, i.e., fully feedforward generators and discriminators with no cycle structures, can also perform on par, with or without paired training data. These encoder-decoder GANs also gain popularity in image enhancement (Wang et al., 2018a; Kupyn et al., 2019; Jiang et al., 2019) and editing (Sanakoyeu et al., 2018; Yang et al., 2019).

Substantial evidences reveal that the generative quality of GANs consistently benefits from larger-scale training (Karras et al., 2017; Brock et al., 2018; Gui et al., 2020). However, the GAN training is notoriously unstable, and therefore numerous techniques were proposed to stabilize the training of increasingly larger GANs, including spectral normalization (Miyato et al., 2018), gradient penalty (Gulrajani et al., 2017) and progressive training (Karras et al., 2017).

Despite their performance boost, the growing complexity of those large GANs conflicts the demands of mobile deployments, calling for compression techniques. To our best knowledge, (Shu et al., 2019) is the only published method in GAN compression at our submission time. The authors experimentally showed that classical channel pruning would fail when applied to compress GANs, due to the sensitivity of GAN training. They thus proposed a co-evolution algorithm to regularize pruning, that relied on the cycle-consistency loss. As a result, their algorithm was restricted to compressing CycleGAN or its variants, and cannot be applied to encoder-decoder GANs. It also needed the trained discriminator of original GANs as part of the pruning loss.

A concurrent work (available after our submission time) that we shall credit is (Li et al., 2020), which also presents AutoML for GAN compression. Its uniqueness lies in searching the channel width for an existing generator. In a “once-for-all” manner, their searched models achieved impressive image translation performance without fine-tuning. In comparison, our proposed AGD framework customizes the search space for tasks with different properties, searches for operators types in addition to channel widths, and additionally evaluates on a super-resolution task.

3 Our AutoGAN-Distiller Framework

Given a pretrained generator over the data , our goal is to obtain a more compact and hardware-friendly generator , from while the generated quality does not sacrifice much, e.g., .

We adopt the differentiable design (Liu et al., 2018a) for our NAS framework. A NAS framework consists of two key components: the search space and the proxy task. AGD customizes both these two components for the specific task of GAN compression, as described in the following sections.

3.1 The Proposed Customized Search Space

General Design Philosophy. Many NAS works adopt the directed acyclic graph (DAG)-type search spaces (Liu et al., 2018a) due to its large volume and flexibility. However, those can result in lots of irregular dense connections. As discussed in (Ma et al., 2018), such network fragmentation is hardware-unfriendly, as it will significantly hamper the parallelism and induce latency/energy overheads. Therefore, we choose a sequential search space, for the ease of implementing the desired parallelism on real mobile devices. In a sequential pipeline, each individual searchable module (a.k.a. a node) only contains a single operator, and they are sequentially connected without any branching.

We jointly search for the operator type and the width of each layer. Different from previous differentiable search (Wu et al., 2019) that selects from predefined building block options where the combinations of operator types and widths are manually picked, we enable each layer’s width and operators to be independently searchable.

Application-specific Supernet. On top of the above general design philosophy, we note that different tasks have developed their own preferred and customized components, calling for instantiating different network architectures per application needs. For example, among common image-to-image GAN tasks, image enhancement prefers deeper networks to recover detailed textures and being precise at pixel-level predictions, while style transfer often refers to shallower architectures, and focuses on consistency of the global style more than local details.

We next derive two specific supernet structures below, for two common tasks: image translation and image super-resolution, respectively. Both follow the general sequential search space design with the efficiency constraint in mind.

  • Image Translation. Our supernet architecture inherits original CycleGAN’s structure (see Fig. 1). We keep the same operators from the stem (the first three convolutional layers) and the header (the last two transposed convolutional layers and one convolutional layer), except searching for their widths. This is motivated by the fact that their downsampling/upsampling structures contribute to lowering the computational complexity of the backbone between them, which have been proved to be effective (Zhu et al., 2017).

    For the backbone part between the stem and header, we adopt nine sequential layers with both searchable operators and widths to trade off between model performance and efficiency. For the normalization layers, we adopt the instance normalization (Ulyanov et al., 2016) that is widely used in image translation (Zhu et al., 2017) and style transfer (Johnson et al., 2016).

    Figure 1: Our supernet for Image Translation, where “TConv” denotes a transposed convolutional layer. We search for both the operators and widths for the layers inside the red box, leaving the stem and header to only search for widths.
  • Super Resolution. Our supernet architecture is inspired by SRResNet (Ledig et al., 2017) where most computation is performed in the low resolution feature space for efficiency (see Fig. 2). We fix the stem and header parts in the original design which amount to computation, and search the remaining residual layers.

    For the residual network structure, ESRGAN (Wang et al., 2018a)

    introduces residual-in-residual (RiR) blocks with dense connections which have higher capacity and is easier to train. Despite improved performance, such densely-connected blocks are hardware-unfriendly. Therefore, we replace the dense blocks in residual-in-residual modules with five sequential layers with both searchable operators and widths. We also remove all the batch normalization layers in the network to eliminate artifacts 

    (Wang et al., 2018a).

    Figure 2: Our supernet for Super Resolution, where “Upconv” denotes bilinearly upsampling followed by a convolutional layer. We search for both the operators and widths for the layers inside the red box, leaving the stem and header fixed.

Operator Search. We search for the following operators:

  • Conv 11;  Conv 33;

  • Residual Block (“ResBlock”) (2 layers of Conv 33, with a skip connection);

  • Depthwise Block (“DwsBlock”) (Conv1 1 + DepthwiseConv 33 + Conv 11, with a skip connection).

These cover popular and efficient building blocks that have been used to construct GAN generators. We did not include dilated convolutions, motivated by their hardware unfriendliness as analyzed in (Ma et al., 2018).

We search for the operator for each layer in a differentiable manner (Liu et al., 2018a; Wu et al., 2019). For the -th layer, we use an architecture parameter to determine the operator for the current layer, and the softmax value of

denotes the probability of choosing this operator. All the candidate operators are activated for every iteration in searching process and the output is the weighted sum of all the operators determined by all the softmax values of all


Width Search. The balance between the capacity of an operator and its width (i.e., number of input/output channels) is a crucial trade-off for a compressed model. Therefore, we also allow for the width search to build a slimmer generator. By searching the widths, we merge the pruning step into the searching process for an end-to-end GAN compression.

However, it is non-trivial to naively build a set of independent convolutional kernels with different widths for each operator, due to the exponentially possible combinations. We thus set a single convolution kernel with a maximal width, named superkernel. Then we search for the expansion ratio to make use of only a subset of the input/output dimensions of the superkernel. We set and use the architecture parameter to control the probability of choosing each expansion ratio in the -th layer. We apply Gumbel-Softmax (Gumbel, 1948) to approximate differentiable sampling for based on as (Cai et al., 2018). Therefore, during the searching process only one most likely expansion ratio will be activated each time, which saves both memory and computation cost.

3.2 The Proposed Customized Proxy Task

During the search process, the NAS framework is guided by the proxy task to optimize the architecture towards the desired patterns. As our objective is to obtain both superior performance and extreme efficiency, we need to customize our proxy task to supervise our search process in a proper way. We formulate the training objective of our AGD framework in Eq. 1:


Here, is a distance metric for the knowledge distillation (Polino et al., 2018) between the compact generator and the pretrained one , is the computational budget determined by the network architecture, and are the architecture parameters controlling the operator and width of each layer, respectively, and is the trade-off parameter. Note that both and are functions of and .

AGD searches for an efficient generator under the guidance of distillation from the original model , through optimizing Eq. 1. Note that, the objective function in Eq. 1 is free of any trained discriminator. We choose so because: (1) in practice, the discriminator is often discarded after the generator is trained, therefore not necessarily available when compressing the generator in future; and (2) we also tried to add a discriminator loss term into Eq. 1, similarly to (Shu et al., 2019) did, but found no improvement in this way (actually, the search becomes more unstable).

One extra hassle arises from our decoupled search of operators and widths. During the search process, if we jointly update and , we observe that our model is prone to suffering from the same “architecture collapse” problem observed in (Chen et al., 2019b), i.e., NAS is quickly biased towards some low-latency yet low-performance models. Therefore, we decouple the computational budget calculation into two individual terms, i.e., the operator-aware and width-aware parts, and weight the two differently:


In order for more flexible model space exploration while enforcing the budget, we set an upper bound and a lower bound for the target computational budget, and double (half) when the computational budget of the derived architecture determined by the current architecture parameters is larger (smaller) than the upper (lower) bound.

The last remaining pieces of our framework are the specific choices of and in Eq. 1. For , we use a combination of content loss (avoid color shift) and perceptual loss (preserve visual and semantic details) as defined in (Johnson et al., 2016), in addition to a total variation loss (enforce pixel-level similarity) (Aly and Dubois, 2005):



are hyperparameters. For

, we apply FLOPs as the computational budget in Eq. 1.

We summarize our AGD framework in Algorithm 1.

1:  Input: dataset , pretrained generator , search space and supernet

, epochs to pretrain (

), search () and train-from-scratch ()
2:  Output: trained efficient generator
3:  Equally split into and and initialize supernet weight and architecture parameters

with uniform distribution

4:  # First Step: Pretrain
5:  for  1 to   do
6:     Get a batch of data from
7:     for  in [, , , ]111We train each operator with the largest width, the smallest width and two random widths during pretraining, following the “sandwich rule” in (Yu and Huang, 2019). do
9:         = update(, )
10:     end for
11:  end for
12:  # Second Step: Search
13:  for  1  to   do
14:     Get a batch of data from
16:      = update(, )
17:     Get a batch of data from
20:      = update(, )
21:      = update(, )
22:  end for
23:  # Third Step: Train from scratch
24:  Derive the searched architecture with maximal for each layer and re-initialize weight .
25:  for  1 to   do
26:     Get a batch of data from
28:      = update(, )
29:  end for
Algorithm 1 The Proposed AutoGAN-Distiller Framework

4 Experiment Results

4.1 Considered Tasks & Models.

Unpaired image-to-image translation. We apply AGD on compressing CycleGAN (Zhu et al., 2017) and consider two datasets, horse2zebra (Zhu et al., 2017) and summer2winter (Zhu et al., 2017). In particular, we individually conduct the architecture search for each task in one dataset. We also consider a special case for AGD that all the weights and activations are quantized to 8-bit integers for hardware-friendly implementation.

Super resolution. We apply AGD on compressing ESRGAN (Wang et al., 2018a) on a combined dataset of DIV2K and Flickr2K (Timofte et al., 2017) with a upscale factor of 4, following (Wang et al., 2018a). We evaluate the searched/compressed model on several popular SR benchmarks, including Set5 (Bevilacqua et al., 2012), Set14 (Zeyde et al., 2010), BSD100 (Martin et al., 2001) and Urban100 (Huang et al., 2015).

4.2 Evaluation Metrics.

Both tasks are mainly evaluated by visual quality. We also use FID (Frechet Inception Distance) (Heusel et al., 2017) for the unpaired image-to-image translation task, and PSNR for super resolution, as quantitative metrics.

For the efficiency aspect, we measure the model size and the inference FLOPs (floating-point operations). As both might not always be aligned with the hardware performance, we further measure the real-device inference latency using NVIDIA GEFORCE RTX 2080 Ti (NVIDIA Inc., ).

4.3 Training details.

NAS Search. The AGD framework adopts the differential search algorithm (Liu et al., 2018a). We split the training dataset into two halves: one for updating supernet weight and the other for updating architecture parameters. The entire search procedure consists of three steps:

  • Pretrain. We only train the supernet weights on the first half dataset. To adapt the supernet for different expansion ratios, we train each operator with the largest width, the smallest width, and two random widths, following the “sandwich rule” in (Yu and Huang, 2019).

  • Search. We then search the architecture by alternatively updating supernet weights and the architecture parameters . During the training, the width of each layer is sampled through Gumbel-Softmax based on and the output is the weighted sum of the results of all operators based on the softmax value of .

  • Derive. We derive the final architecture by choosing the operator and width with the maximal probability for each layer, determined by and . We then train the derived architecture from scratch.

Unpaired image-to-image translation. For AGD on CycleGAN, in Eq. 1 is , and in Eq. 2 are set to 1/4 and 3/4, and and in Eq. 3 are set to be , 1, and , respectively. We pretrain and search for 50 epochs, with batch size 2. We use an SGD optimizer with a momentum of 0.9 and the initial learning rate for the weights, which linearly decays to 0 after 10 epochs, and an Adam optimizer with a constant learning rate for architecture parameters. We train the searched architecture from scratch for 400 epochs, with a batch size of 16 and an initial learning rate of , which linearly decays to 0 after 100 epochs.

Super resolution. For AGD on ESRGAN, in Eq. 1 is , and in Eq. 2 are 2/7 and 5/7, and , and in Eq. 3 are set to be , 1, and , respectively. We pretrain and search for 100 epochs, with each batch of 6 cropped patches with . We set the initial learning rate for the weights to be with an Adam optimizer, decayed by 0.5 at the 25-th, 50-th, and 75-th epoch, and that for the architecture parameters is a constant of . To train the searched architecture from scratch, we train for 1800 epochs with a learning rate of , decayed by 0.5 at 225-th, 450-th, 900-th, and 1300-th epoch, following  (Wang et al., 2018a).

GFLOPs 54.17 Latency (ms) 7.25
Memory (MB) 43.51
FID horse2zebra 74.04
zebra2horse 148.81
summer2winter 79.12
winter2summer 73.31
Table 1: Statistics of the original CycleGAN (Zhu et al., 2017) on different unpaired image translation tasks, where the latency is measured on NVIDIA GEFORCE RTX 2080 Ti. Note that the FID of CycleGAN is from CEC (Shu et al., 2019) since FID (Heusel et al., 2017) is proposed after CycleGAN (Zhu et al., 2017).

4.4 AGD for Efficient Unpaired Image Translation

For unpaired image translation, the original performance and efficiency statistics of CycleGAN are reported in Table 1. We compare our AGD with CEC (Shu et al., 2019), the existing GAN compression algorithm specifically for CycleGAN; we also include classical structural pruning (Li et al., 2016) developed for classification models222We prune 70% filters with smallest -norm values in each layer, and then finetune the remaining weights..

Quantitative Results. Table 2 demonstrates that for all tasks, AGD consistently achieves significantly better FID than the state-of-the-art CEC, with even higher savings of model sizes and FLOPs. Besides, the performance of structural pruning, which is effective for compressing classification models, lags far behind with CEC, not to say AGD. That re-confirms (Shu et al., 2019)’s finding that existing classification-oriented compression algorithms cannot be directly plugged into compressing GANs.

Furthermore, the gain of AGD also manifests in real-device latency measurements. For example, on the summer2winter dataset, the per-image inference latency of the AGD-compressed model is reduced by 59.86% with an even slightly better FID than the original CycleGAN.

Dataset Method FID GFlops Memory (MB) Latency (ms)
horse2zebra Prune 220.91 4.95 3.91 6.19
CEC 96.15 13.45 10.16 -
AGD 83.6 6.39 4.48 4.07
zebra2horse Prune 206.56 4.95 3.91 6.19
CEC 157.9 13.06 10 -
AGD 137.2 4.84 3.2 3.99
summer2winter Prune 120.94 4.95 3.91 6.19
CEC 78.58 12.98 7.98 -
AGD 78.33 4.34 2.72 2.91
winter2summer Prune 106.2 4.95 3.91 6.19
CEC 79.26 16.45 7.61 -
AGD 77.73 4.26 2.64 4.26
Table 2: Quantitative comparison with the structural pruning (Li et al., 2016) and CEC (Shu et al., 2019) on CycleGAN compression in different unpaired image translation tasks.
Source images Original CycleGAN CEC (Shu et al., 2019) AGD (Proposed)
54.17 GFLOPs
43.51 MB
13.45 GFLOPs
10.16 MB
6.39 GFLOPs
4.48 MB
54.17 GFLOPs
43.51 MB
13.06 GFLOPs
10.00 MB
4.84 GFLOPs
3.20 MB
54.17 GFLOPs
43.51 MB
12.98 GFLOPs
7.98 MB
4.34 GFLOPs
2.72 MB
Figure 3: Visualization examples of CycleGAN compression on the horse2zebra task (rows 1 and 2), zebra2horse task (row 3), and summer2winter task (row 4). Columns from left to right: source images, translation results by original CycleGAN, CEC, and AGD, respectively, and the FLOPs and memory (model size) of each method on each task are annotated above the images.

Visualization Results. We compare the visual quality from the original CycleGAN, CEC, and AGD in Fig. 3, using examples from the horse2zebra and zebra2horse tasks. The AGD-compressed CycleGAN yields comparable visual quality to the uncompressed model in all cases. With zooming-in, AGD also appears to visually outperform CEC, in producing sharper edges and textural details, suppressing checkboard artifacts, as well as eliminating color distortion.

Block ID Stem0 Stem1 Stem2 B1 B2
(OP, Width) (-, 88) (-, 88) (-, 88) (DwsBlock, 88) (ResBlock, 88)
Block ID B3 B4 B5 B6 B7
(OP, Width) (ResBlock, 88) (ResBlock, 128) (ResBlock, 88) (DwsBlock, 128) (Conv1x1, 216)
Block ID B8 B9 Header1 Hearder2 Header3
(OP, Width) (Conv1x1, 88) (Conv1x1, 128) (-, 216) (-, 88) (-, 3)
Table 3: The searched architecture (operator, width) by our AGD framework on CycleGAN.

AGD Searched Architecture. The searched architecture by AGD on the horse2zebra dataset is outlined in Table 3. AGD selects the “heavier” DwsBlocks and ResBlocks with narrower channel widths for the early layers; while leaving the last three layers to be Conv 1x1, with larger widths. The searched design remains to be overall light-weighted, while not “collapsing” to overly under-parameterized, poor performance models. Similar architectures searched on the other three tasks are shown in our supplementary materials.

Figure 4: Visualization results of CycleGAN compressed by AGD with quantization, where the first two figures are from the horse2zebra task,and the last figure is from the zebra2horse task.

Quantization. We also conduct an extended study on how our AGD compression work with quantization. On the AGD-compressed CycleGAN, we further quantize all the weights and activations to 8-bit integers, following (Banner et al., 2018). The quantitative and visualization results are shown in Table 4 and Fig. 4, respectively: after being further aggressively compressed, the models maintain both competitive FID and comparable visualization quality.

Datasets FID Memory (MB)
horse2zebra 85.74 1.00
zebra2horse 140.08 1.24
Table 4: Performance of AGD framework on CycleGAN compression with 8-bit quantization.
Original ESRGAN Pruned ESRGAN SRGAN VDSR AGD (Proposed)
1176.61 GFLOPs
66.8 MB
113.07 GFLOPs
6.40 MB
166.66 GFLOPs
6.08 MB
699.36 GFLOPs
2.67 MB
108.06 GFLOPs
1.64 MB
Figure 5: Visualization results of different super resolution methods (Better viewed after zooming in). From top to bottom rows are examples from Set5, Set14, BSD100, and Urban100. Columns from left to right: original ESRGAN (Wang et al., 2018a), ESRGAN after structural pruning (Li et al., 2016), SRGAN (Ledig et al., 2017), VDSR (Kim et al., 2016), and ESRGAN compressed by AGD. FLOPs (calculated as processing a 256256 low resolution image with a scale factor of four) and memory (model size) of each method on each task are annotated above the images.

4.5 AGD for Efficient Super Resolution

We then apply AGD on compressing ESRGAN (Wang et al., 2018a), a start-of-the-art GAN for super resolution. ESRGAN does not adopt a CycleGAN-type structure (its generator is a feedforward encoder-decoder), therefore CEC (Shu et al., 2019) is not applicable. We compare AGD with a structural pruning (Li et al., 2016) baseline, and also include two other high-performance and lighter SR models: SRGAN (Ledig et al., 2017) and VDSR (Kim et al., 2016), to better evaluate the utility-efficiency trade-off of our compressed models. Besides, since visual quality is the ultimate demand in SR, we first adopt the visualization-oriented ESRGAN333ESRGAN (Wang et al., 2018a) provides two pretrained models, including the PSNR-oriented one trained only with the content loss, targeted for higher PSNR performance; and the visualization-oriented one trained with both content loss and perceptual loss, targeted for higher perceptual quality. as the teacher model in Eq. 3.

GFLOPs (256x256) 1176.61 113.07 166.66 108.6
Memory (MB) 66.8 6.40 6.08 1.64
Set5 PSNR 30.47 28.07 29.40 30.44
Latency (ms) 84.62 64.75 4.92 5.08
Set14 PSNR 26.29 25.21 26.02 27.28
Latency (ms) 80.03 78.47 5.37 6.91
BSD100 PSNR 25.32 24.74 25.16 26.23
Latency (ms) 81.34 69.43 4.15 5.11
Urban100 PSNR 24.36 22.67 24.39 24.74
Latency (ms) 184.79 113.82 25.28 21.60
Table 5: Quantitative comparison with the state-of-the-art GAN-based visualization-oriented SR models, where the FLOPs are calculated as processing a 256256 low resolution image with a scale factor of four.

Quantitative Results. Table 5 shows the quantitative comparison in PSNR of a series of GAN-based super resolution methods, re-confirming the little-to-no performance loss of AGD on ESRGAN (interestingly, sometimes even PSNR improvement is seen). Compared to the off-the-shelf alternatives (e.g., SRGAN), AGD compressed ESRGAN obtains consistently superior PSNRs, with a smaller model size and comparable real-device latency.

Visualization Results. As shown in Fig. 5, AGD compression dramatically reduces the model size by over 40 times, without sacrificing the visual quality. The real device latency is also shrunk by 10-18 times on the four sets.

RiR Block ID OP1 OP2 OP3 OP4 OP5
1 (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 64)
2 (DwsBlock, 24) (DwsBlock, 24) (ResBlock, 64) (Conv3x3, 24) (ResBlock, 64)
3 (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 64)
4 (Conv3x3, 32) (DwsBlock, 32) (DwsBlock, 24) (DwsBlock, 24) (ResBlock, 64)
5 (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 64)
Table 6: Searched architecture (operator, width) by AGD on visualization-oriented ESRGAN, where we search for five operators and their widths in the five Residual-in-Residual blocks.

AGD Searched Architecture. As shown in Table 6, we find that among the original five RiR blocks, AGD assigns more computationally powerful operators to the 2nd and 4th one, while keeping the other three simple by only using Conv operations, achieving a proper balance between heavy and efficient operations.

RiR Block ID OP1 OP2 OP3 OP4 OP5
1 (DwsBlock, 32) (DwsBlock, 32) (DwsBlock, 48) (DwsBlock, 24) (ResBlock, 64)
2 (DwsBlock, 24) (ResBlock, 32) (Conv1x1, 32) (Conv1x1, 24) (Conv3x3, 64)
3 (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 64)
4 (DwsBlock, 32) (DwsBlock, 48) (DwsBlock, 24) (DwsBlock, 24) (ResBlock, 64)
5 (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 24) (Conv1x1, 64)
Table 7: Searched architecture (operator, width) by our proposed AGD on the PSNR-oriented ESRGAN (Wang et al., 2018a), where RiR denotes a Residual-in-Residual block using the operators in our search space. We search for five operators and their widths in each RiR block (totally five RiR blocks).

Comparison with PSNR-oriented SR Methods. We also search for a new architecture as shown in Table 7 under the distillation of a PSNR-oriented ESRGAN with the content loss only in Eq. 3 (following (Wang et al., 2018a)) within the same search space. The quantitative results of the original ESRGAN (Wang et al., 2018a), the searched architecture of AGD, and the PSNR-oriented model VSDR (Kim et al., 2016) are shown in Table 8. Compared with VDSR, AGD achieves better PSNR (up to 0.44) on all the four datasets with 84.1% fewer FLOPs and 32.6% smaller model size.

5 Conclusion

We propose the AGD framework to significantly push forward the frontier of GAN compression by introducing AutoML here. Starting with a specially designed search space of efficient building blocks, AGD performs differential neural architecture search under both the guidance of knowledge distillation and the constraint of computational resources. AGD is automatic with no assumptions about GAN structures, loss forms, or the availability of discriminators, and can be applicable to various GAN tasks. Experiments show that AGD outperforms existing options with aggressively reduced FLOPs, model size, and real-device latency, without degrading model performance. Our future work include evaluating AGD in more GAN-based applications such as data augmentation (Zhang et al., 2019).

Model GFLOPs (256x256) Memory (MB) PSNR
Set5 Set14 BSD100 Urban100
ESRGAN 1176.61 66.8 32.73 28.99 27.85 27.03
VDSR 699.36 2.67 31.35 28.01 27.29 25.18
AGD 110.9 1.8 31.79 28.36 27.41 25.55
Table 8: Quantitative comparison with the state-of-the-art PSNR-oriented SR models. FLOPs are calculated as processing a 256256 low resolution image with a scale factor of four.


The work is supported by the National Science Foundation (NSF) through the Energy, Power, Control, and Networks (EPCN) program (Award number: 1934755, 1934767).


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