910 citations · 1.7k across the 16 of their papers we have counts for
16 papers
MixupE: Understanding and Improving Mixup from Directional Derivative Perspective
Yingtian Zou, Vikas Verma, Sarthak Mittal +6
Mixup is a popular data augmentation technique for training deep neural networks where additional samples are generated by linearly interpolating pairs of inputs and their labels.…
CNT (Conditioning on Noisy Targets): A new Algorithm for Leveraging Top-Down Feedback
Alexia Jolicoeur-Martineau, Alex Lamb, Vikas Verma +1
We propose a novel regularizer for supervised learning called Conditioning on Noisy Targets (CNT). This approach consists in conditioning the model on a noisy version of the target…
Towards Domain-Agnostic Contrastive Learning
Vikas Verma, Minh-Thang Luong, Kenji Kawaguchi +2
Despite recent success, most contrastive self-supervised learning methods are domain-specific, relying heavily on data augmentation techniques that require knowledge about a partic…
PatchUp: A Feature-Space Block-Level Regularization Technique for Convolutional Neural Networks
Mojtaba Faramarzi, Mohammad Amini, Akilesh Badrinaaraayanan +2
Large capacity deep learning models are often prone to a high generalization gap when trained with a limited amount of labeled training data. A recent class of methods to address t…
Interpolation-based semi-supervised learning for object detection
Jisoo Jeong, Vikas Verma, Minsung Hyun +2
Despite the data labeling cost for the object detection tasks being substantially more than that of the classification tasks, semi-supervised learning methods for object detection…
SketchTransfer: A Challenging New Task for Exploring Detail-Invariance and the Abstractions Learned by Deep Networks
Alex Lamb, Sherjil Ozair, Vikas Verma +1
Deep networks have achieved excellent results in perceptual tasks, yet their ability to generalize to variations not seen during training has come under increasing scrutiny. In thi…