10 citations · 15 across the 7 of their papers we have counts for
3 papers · 1 filter
Balancing Robustness and Sensitivity using Feature Contrastive Learning
Seungyeon Kim, Daniel Glasner, Srikumar Ramalingam +3
It is generally believed that robust training of extremely large networks is critical to their success in real-world applications. However, when taken to the extreme, methods that…
Less is more: Selecting informative and diverse subsets with balancing constraints
Srikumar Ramalingam, Daniel Glasner, Kaushal Patel +3
Deep learning has yielded extraordinary results in vision and natural language processing, but this achievement comes at a cost. Most models require enormous resources during train…
Understanding Robustness of Transformers for Image Classification
Srinadh Bhojanapalli, Ayan Chakrabarti, Daniel Glasner +3
Deep Convolutional Neural Networks (CNNs) have long been the architecture of choice for computer vision tasks. Recently, Transformer-based architectures like Vision Transformer (Vi…