37 citations · 60 across the 21 of their papers we have counts for
21 papers
Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method
Kishaan Jeeveswaran, Elahe Arani, Bahram Zonooz
Domain incremental learning (DIL) poses a significant challenge in real-world scenarios, as models need to be sequentially trained on diverse domains over time, all the while avoid…
IMEX-Reg: Implicit-Explicit Regularization in the Function Space for Continual Learning
Prashant Bhat, Bharath Renjith, Elahe Arani +1
Continual learning (CL) remains one of the long-standing challenges for deep neural networks due to catastrophic forgetting of previously acquired knowledge. Although rehearsal-bas…
Can We Break Free from Strong Data Augmentations in Self-Supervised Learning?
Shruthi Gowda, Elahe Arani, Bahram Zonooz
Self-supervised learning (SSL) has emerged as a promising solution for addressing the challenge of limited labeled data in deep neural networks (DNNs), offering scalability potenti…
The Effectiveness of Random Forgetting for Robust Generalization
Vijaya Raghavan T Ramkumar, Bahram Zonooz, Elahe Arani
Deep neural networks are susceptible to adversarial attacks, which can compromise their performance and accuracy. Adversarial Training (AT) has emerged as a popular approach for pr…
Conserve-Update-Revise to Cure Generalization and Robustness Trade-off in Adversarial Training
Shruthi Gowda, Bahram Zonooz, Elahe Arani
Adversarial training improves the robustness of neural networks against adversarial attacks, albeit at the expense of the trade-off between standard and robust generalization. To u…
Continual Learning of Unsupervised Monocular Depth from Videos
Hemang Chawla, Arnav Varma, Elahe Arani +1
Spatial scene understanding, including monocular depth estimation, is an important problem in various applications, such as robotics and autonomous driving. While improvements in u…