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12/27/2022
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Mixup is a popular data augmentation technique for training deep neural ...
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10/18/2022
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback
We propose a novel regularizer for supervised learning called Conditioni...
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11/09/2020
Towards Domain-Agnostic Contrastive Learning
Despite recent success, most contrastive self-supervised learning method...
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06/14/2020
PatchUp: A Regularization Technique for Convolutional Neural Networks
Large capacity deep learning models are often prone to a high generaliza...
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06/03/2020
Interpolation-based semi-supervised learning for object detection
Despite the data labeling cost for the object detection tasks being subs...
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12/25/2019
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Deep networks have achieved excellent results in perceptual tasks, yet t...
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09/25/2019
GraphMix: Regularized Training of Graph Neural Networks for Semi-Supervised Learning
We present GraphMix, a regularization technique for Graph Neural Network...
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07/16/2019
Towards Understanding Generalization in Gradient-Based Meta-Learning
In this work we study generalization of neural networks in gradient-base...
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06/16/2019
Interpolated Adversarial Training: Achieving Robust Neural Networks without Sacrificing Accuracy
Adversarial robustness has become a central goal in deep learning, both ...
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03/09/2019
Interpolation Consistency Training for Semi-Supervised Learning
We introduce Interpolation Consistency Training (ICT), a simple and comp...
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03/07/2019
Adversarial Mixup Resynthesizers
In this paper, we explore new approaches to combining information encode...
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06/18/2018
Modularity Matters: Learning Invariant Relational Reasoning Tasks
We focus on two supervised visual reasoning tasks whose labels encode a ...
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06/13/2018
Manifold Mixup: Encouraging Meaningful On-Manifold Interpolation as a Regularizer
Deep networks often perform well on the data manifold on which they are ...
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10/13/2017
Residual Connections Encourage Iterative Inference
Residual networks (Resnets) have become a prominent architecture in deep...
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02/28/2017
Deep Semi-Random Features for Nonlinear Function Approximation
We propose semi-random features for nonlinear function approximation. Th...
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09/02/2014
