33 citations · 55 across the 5 of their papers we have counts for
8 papers · 1 filter
Backdoor Attacks on Vision Transformers
Akshayvarun Subramanya, Aniruddha Saha, Soroush Abbasi Koohpayegani +2
Vision Transformers (ViT) have recently demonstrated exemplary performance on a variety of vision tasks and are being used as an alternative to CNNs. Their design is based on a sel…
A Simple Approach to Adversarial Robustness in Few-shot Image Classification
Akshayvarun Subramanya, Hamed Pirsiavash
Few-shot image classification, where the goal is to generalize to tasks with limited labeled data, has seen great progress over the years. However, the classifiers are vulnerable t…
Constrained Mean Shift Using Distant Yet Related Neighbors for Representation Learning
KL Navaneet, Soroush Abbasi Koohpayegani, Ajinkya Tejankar +3
We are interested in representation learning in self-supervised, supervised, and semi-supervised settings. Some recent self-supervised learning methods like mean-shift (MSF) cluste…
Role of Spatial Context in Adversarial Robustness for Object Detection
Aniruddha Saha, Akshayvarun Subramanya, Koninika Patil +1
The benefits of utilizing spatial context in fast object detection algorithms have been studied extensively. Detectors increase inference speed by doing a single forward pass per i…
Hidden Trigger Backdoor Attacks
Aniruddha Saha, Akshayvarun Subramanya, Hamed Pirsiavash
With the success of deep learning algorithms in various domains, studying adversarial attacks to secure deep models in real world applications has become an important research topi…
Fooling Network Interpretation in Image Classification
Akshayvarun Subramanya, Vipin Pillai, Hamed Pirsiavash
Deep neural networks have been shown to be fooled rather easily using adversarial attack algorithms. Practical methods such as adversarial patches have been shown to be extremely e…
Confidence estimation in Deep Neural networks via density modelling
Akshayvarun Subramanya, Suraj Srinivas, R. Venkatesh Babu
State-of-the-art Deep Neural Networks can be easily fooled into providing incorrect high-confidence predictions for images with small amounts of adversarial noise. Does this expose…
Training Sparse Neural Networks
Suraj Srinivas, Akshayvarun Subramanya, R. Venkatesh Babu
Deep neural networks with lots of parameters are typically used for large-scale computer vision tasks such as image classification. This is a result of using dense matrix multiplic…