16 citations · 20 across the 18 of their papers we have counts for
7 papers · 1 filter
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…
Mitigating Interference in the Knowledge Continuum through Attention-Guided Incremental Learning
Prashant Bhat, Bharath Renjith, Elahe Arani +1
Continual learning (CL) remains a significant challenge for deep neural networks, as it is prone to forgetting previously acquired knowledge. Several approaches have been proposed…
Beyond Unimodal Learning: The Importance of Integrating Multiple Modalities for Lifelong Learning
Fahad Sarfraz, Bahram Zonooz, Elahe Arani
While humans excel at continual learning (CL), deep neural networks (DNNs) exhibit catastrophic forgetting. A salient feature of the brain that allows effective CL is that it utili…
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…