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Switch Transformers: Scaling to Trillion Parameter Models with Simple and Efficient Sparsity
In deep learning, models typically reuse the same parameters for all inp...
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Simple Copy-Paste is a Strong Data Augmentation Method for Instance Segmentation
Building instance segmentation models that are data-efficient and can ha...
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Does Data Augmentation Benefit from Split BatchNorms
Data augmentation has emerged as a powerful technique for improving the ...
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Rethinking Pre-training and Self-training
Pre-training is a dominant paradigm in computer vision. For example, sup...
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Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Supervised learning in large discriminative models is a mainstay for mod...
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Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
Supervised learning in large discriminative models is a mainstay for mod...
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Improving 3D Object Detection through Progressive Population Based Augmentation
Data augmentation has been widely adopted for object detection in 3D poi...
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AugMix: A Simple Data Processing Method to Improve Robustness and Uncertainty
Modern deep neural networks can achieve high accuracy when the training ...
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RandAugment: Practical data augmentation with no separate search
Recent work has shown that data augmentation has the potential to signif...
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Learning Data Augmentation Strategies for Object Detection
Data augmentation is a critical component of training deep learning mode...
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Attention Augmented Convolutional Networks
Convolutional networks have been the paradigm of choice in many computer...
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SpecAugment: A Simple Data Augmentation Method for Automatic Speech Recognition
We present SpecAugment, a simple data augmentation method for speech rec...
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Searching for Efficient Multi-Scale Architectures for Dense Image Prediction
The design of neural network architectures is an important component for...
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Backprop Evolution
The back-propagation algorithm is the cornerstone of deep learning. Desp...
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AutoAugment: Learning Augmentation Policies from Data
In this paper, we take a closer look at data augmentation for images, an...
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Efficient Neural Architecture Search via Parameter Sharing
We propose Efficient Neural Architecture Search (ENAS), a fast and inexp...
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Efficient Neural Architecture Search via Parameters Sharing
We propose Efficient Neural Architecture Search (ENAS), a fast and inexp...
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Progressive Neural Architecture Search
We propose a method for learning CNN structures that is more efficient t...
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Intriguing Properties of Adversarial Examples
It is becoming increasingly clear that many machine learning classifiers...
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Searching for Activation Functions
The choice of activation functions in deep networks has a significant ef...
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Neural Optimizer Search with Reinforcement Learning
We present an approach to automate the process of discovering optimizati...
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Learning Transferable Architectures for Scalable Image Recognition
Developing neural network image classification models often requires sig...
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Neural Architecture Search with Reinforcement Learning
Neural networks are powerful and flexible models that work well for many...
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Transfer Learning for Low-Resource Neural Machine Translation
The encoder-decoder framework for neural machine translation (NMT) has b...
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Multi-Source Neural Translation
We build a multi-source machine translation model and train it to maximi...
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