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Ultra-Data-Efficient GAN Training: Drawing A Lottery Ticket First, Then Training It Toughly
Training generative adversarial networks (GANs) with limited data genera...
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Exposing Semantic Segmentation Failures via Maximum Discrepancy Competition
Semantic segmentation is an extensively studied task in computer vision,...
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Neural Architecture Search on ImageNet in Four GPU Hours: A Theoretically Inspired Perspective
Neural Architecture Search (NAS) has been explosively studied to automat...
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Sandwich Batch Normalization
We present Sandwich Batch Normalization (SaBN), an embarrassingly easy i...
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Weak NAS Predictors Are All You Need
Neural Architecture Search (NAS) finds the best network architecture by ...
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TransGAN: Two Transformers Can Make One Strong GAN
The recent explosive interest on transformers has suggested their potent...
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A Unified Lottery Ticket Hypothesis for Graph Neural Networks
With graphs rapidly growing in size and deeper graph neural networks (GN...
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On Dynamic Noise Influence in Differentially Private Learning
Protecting privacy in learning while maintaining the model performance h...
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Good Students Play Big Lottery Better
Lottery ticket hypothesis suggests that a dense neural network contains ...
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SmartDeal: Re-Modeling Deep Network Weights for Efficient Inference and Training
The record-breaking performance of deep neural networks (DNNs) comes wit...
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EarlyBERT: Efficient BERT Training via Early-bird Lottery Tickets
Deep, heavily overparameterized language models such as BERT, XLNet and ...
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Growing Deep Forests Efficiently with Soft Routing and Learned Connectivity
Despite the latest prevailing success of deep neural networks (DNNs), se...
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FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training
Recent breakthroughs in deep neural networks (DNNs) have fueled a tremen...
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The Lottery Tickets Hypothesis for Supervised and Self-supervised Pre-training in Computer Vision Models
The computer vision world has been re-gaining enthusiasm in various pre-...
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Uncertainty-Aware Physically-Guided Proxy Tasks for Unseen Domain Face Anti-spoofing
Face anti-spoofing (FAS) seeks to discriminate genuine faces from fake o...
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What Does CNN Shift Invariance Look Like? A Visualization Study
Feature extraction with convolutional neural networks (CNNs) is a popula...
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Robust Pre-Training by Adversarial Contrastive Learning
Recent work has shown that, when integrated with adversarial training, s...
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ShiftAddNet: A Hardware-Inspired Deep Network
Multiplication (e.g., convolution) is arguably a cornerstone of modern d...
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Graph Contrastive Learning with Augmentations
Generalizable, transferrable, and robust representation learning on grap...
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Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free
Adversarial training and its many variants substantially improve deep ne...
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Training Stronger Baselines for Learning to Optimize
Learning to optimize (L2O) has gained increasing attention since classic...
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MM-Hand: 3D-Aware Multi-Modal Guided Hand Generative Network for 3D Hand Pose Synthesis
Estimating the 3D hand pose from a monocular RGB image is important but ...
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GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Generative adversarial networks (GANs) have gained increasing popularity...
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The Lottery Ticket Hypothesis for Pre-trained BERT Networks
In natural language processing (NLP), enormous pre-trained models like B...
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Automated Synthetic-to-Real Generalization
Models trained on synthetic images often face degraded generalization to...
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Can 3D Adversarial Logos Cloak Humans?
With the trend of adversarial attacks, researchers attempt to fool train...
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When Does Self-Supervision Help Graph Convolutional Networks?
Self-supervision as an emerging technique has been employed to train con...
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AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
The compression of Generative Adversarial Networks (GANs) has lately dra...
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NADS: Neural Architecture Distribution Search for Uncertainty Awareness
Machine learning (ML) systems often encounter Out-of-Distribution (OoD) ...
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Focus Longer to See Better:Recursively Refined Attention for Fine-Grained Image Classification
Deep Neural Network has shown great strides in the coarse-grained image ...
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SmartExchange: Trading Higher-cost Memory Storage/Access for Lower-cost Computation
We present SmartExchange, an algorithm-hardware co-design framework to t...
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AutoSpeech: Neural Architecture Search for Speaker Recognition
Speaker recognition systems based on Convolutional Neural Networks (CNNs...
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L^2-GCN: Layer-Wise and Learned Efficient Training of Graph Convolutional Networks
Graph convolution networks (GCN) are increasingly popular in many applic...
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Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning
Pretrained models from self-supervision are prevalently used in fine-tun...
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Safeguarded Learned Convex Optimization
Many applications require repeatedly solving a certain type of optimizat...
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I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
The learning of hierarchical representations for image classification ha...
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Triple Wins: Boosting Accuracy, Robustness and Efficiency Together by Enabling Input-Adaptive Inference
Deep networks were recently suggested to face the odds between accuracy ...
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VGAI: A Vision-Based Decentralized Controller Learning Framework for Robot Swarms
Despite the popularity of decentralized controller learning, very few su...
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Deep Plastic Surgery: Robust and Controllable Image Editing with Human-Drawn Sketches
Sketch-based image editing aims to synthesize and modify photos based on...
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Fractional Skipping: Towards Finer-Grained Dynamic CNN Inference
While increasingly deep networks are still in general desired for achiev...
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Explainable Deep Relational Networks for Predicting Compound-Protein Affinities and Contacts
Predicting compound-protein affinity is critical for accelerating drug d...
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FasterSeg: Searching for Faster Real-time Semantic Segmentation
We present FasterSeg, an automatically designed semantic segmentation ne...
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Practical Solutions for Machine Learning Safety in Autonomous Vehicles
Autonomous vehicles rely on machine learning to solve challenging tasks ...
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In Defense of the Triplet Loss Again: Learning Robust Person Re-Identification with Fast Approximated Triplet Loss and Label Distillation
The comparative losses (typically, triplet loss) are appealing choices f...
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DAVID: Dual-Attentional Video Deblurring
Blind video deblurring restores sharp frames from a blurry sequence with...
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Calibrated Domain-Invariant Learning for Highly Generalizable Large Scale Re-Identification
Many real-world applications, such as city-scale traffic monitoring and ...
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Learning to Optimize in Swarms
Learning to optimize has emerged as a powerful framework for various opt...
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E2-Train: Energy-Efficient Deep Network Training with Data-, Model-, and Algorithm-Level Saving
Convolutional neural networks (CNNs) have been increasingly deployed to ...
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Drawing early-bird tickets: Towards more efficient training of deep networks
(Frankle & Carbin, 2019) shows that there exist winning tickets (small b...
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Delving into Robust Object Detection from Unmanned Aerial Vehicles: A Deep Nuisance Disentanglement Approach
Object detection from images captured by Unmanned Aerial Vehicles (UAVs)...
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