2 citations · 3 across the 4 of their papers we have counts for
4 papers
Dropout Inference with Non-Uniform Weight Scaling
Zhaoyuan Yang, Arpit Jain
Dropout as regularization has been used extensively to prevent overfitting for training neural networks. During training, units and their connections are randomly dropped, which co…
Learning from Synthetic Data: Addressing Domain Shift for Semantic Segmentation
Swami Sankaranarayanan, Yogesh Balaji, Arpit Jain +2
Visual Domain Adaptation is a problem of immense importance in computer vision. Previous approaches showcase the inability of even deep neural networks to learn informative represe…
Regularizing deep networks using efficient layerwise adversarial training
Swami Sankaranarayanan, Arpit Jain, Rama Chellappa +1
Adversarial training has been shown to regularize deep neural networks in addition to increasing their robustness to adversarial examples. However, its impact on very deep state of…
Self corrective Perturbations for Semantic Segmentation and Classification
Swami Sankaranarayanan, Arpit Jain, Ser Nam Lim
Convolutional Neural Networks have been a subject of great importance over the past decade and great strides have been made in their utility for producing state of the art performa…