most citedA reading survey on adversarial machine learning: Adversarial attacks and their understanding

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2024

Linking Robustness and Generalization: A k* Distribution Analysis of Concept Clustering in Latent Space for Vision Models

Shashank Kotyan, Pin-Yu Chen, Danilo Vasconcellos Vargas

Most evaluations of vision models use indirect methods to assess latent space quality. These methods often involve adding extra layers to project the latent space into a new one. T…

cs.LG2023

Towards Improving Robustness Against Common Corruptions using Mixture of Class Specific Experts

Shashank Kotyan, Danilo Vasconcellos Vargas

Neural networks have demonstrated significant accuracy across various domains, yet their vulnerability to subtle input alterations remains a persistent challenge. Conventional meth…

cs.CV2023

Towards Improving Robustness Against Common Corruptions in Object Detectors Using Adversarial Contrastive Learning

Shashank Kotyan, Danilo Vasconcellos Vargas

Neural networks have revolutionized various domains, exhibiting remarkable accuracy in tasks like natural language processing and computer vision. However, their vulnerability to s…

cs.CV2023

Improving Robustness for Vision Transformer with a Simple Dynamic Scanning Augmentation

Shashank Kotyan, Danilo Vasconcellos Vargas

Vision Transformer (ViT) has demonstrated promising performance in computer vision tasks, comparable to state-of-the-art neural networks. Yet, this new type of deep neural network…

cs.LG20233 cited

A reading survey on adversarial machine learning: Adversarial attacks and their understanding

Shashank Kotyan

Deep Learning has empowered us to train neural networks for complex data with high performance. However, with the growing research, several vulnerabilities in neural networks have…