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10/19/2022
G-Augment: Searching for the Meta-Structure of Data Augmentation Policies for ASR
Data augmentation is a ubiquitous technique used to provide robustness t...
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08/25/2021
Multi-Task Self-Training for Learning General Representations
Despite the fast progress in training specialized models for various tas...
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03/13/2021
Revisiting ResNets: Improved Training and Scaling Strategies
Novel computer vision architectures monopolize the spotlight, but the im...
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12/13/2020
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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12/05/2020
Crystal Structure Search with Random Relaxations Using Graph Networks
Materials design enables technologies critical to humanity, including co...
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09/17/2020
Kohn-Sham equations as regularizer: building prior knowledge into machine-learned physics
Including prior knowledge is important for effective machine learning mo...
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06/11/2020
Rethinking Pre-training and Self-training
Pre-training is a dominant paradigm in computer vision. For example, sup...
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05/20/2020
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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05/20/2020
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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02/20/2020
Affinity and Diversity: Quantifying Mechanisms of Data Augmentation
Though data augmentation has become a standard component of deep neural ...
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01/21/2020
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging...
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12/09/2019
JAX, M.D.: End-to-End Differentiable, Hardware Accelerated, Molecular Dynamics in Pure Python
A large fraction of computational science involves simulating the dynami...
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12/05/2019
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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11/21/2019
ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
We improve the recently-proposed "MixMatch" semi-supervised learning alg...
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09/30/2019
RandAugment: Practical data augmentation with no separate search
Recent work has shown that data augmentation has the potential to signif...
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06/26/2019
Learning Data Augmentation Strategies for Object Detection
Data augmentation is a critical component of training deep learning mode...
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06/21/2019
A Fourier Perspective on Model Robustness in Computer Vision
Achieving robustness to distributional shift is a longstanding and chall...
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06/08/2019
Using learned optimizers to make models robust to input noise
State-of-the art vision models can achieve superhuman performance on ima...
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06/06/2019
Improving Robustness Without Sacrificing Accuracy with Patch Gaussian Augmentation
Deploying machine learning systems in the real world requires both high ...
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04/18/2019
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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05/24/2018
AutoAugment: Learning Augmentation Policies from Data
In this paper, we take a closer look at data augmentation for images, an...
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04/24/2018
Realistic Evaluation of Deep Semi-Supervised Learning Algorithms
Semi-supervised learning (SSL) provides a powerful framework for leverag...
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11/08/2017
