62 citations · 153 across the 45 of their papers we have counts for
16 papers · 1 filter
Continual Learning Beyond Experience Rehearsal and Full Model Surrogates
Prashant Bhat, Laurens Niesten, Elahe Arani +1
Continual learning (CL) has remained a significant challenge for deep neural networks as learning new tasks erases previously acquired knowledge, either partially or completely. Ex…
Parameter Efficient Continual Learning with Dynamic Low-Rank Adaptation
Prashant Shivaram Bhat, Shakib Yazdani, Elahe Arani +1
Catastrophic forgetting has remained a critical challenge for deep neural networks in Continual Learning (CL) as it undermines consolidated knowledge when learning new tasks. Param…
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…
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…
Multi-Task Structural Learning using Local Task Similarity induced Neuron Creation and Removal
Naresh Kumar Gurulingan, Bahram Zonooz, Elahe Arani
Multi-task learning has the potential to improve generalization by maximizing positive transfer between tasks while reducing task interference. Fully achieving this potential is hi…
Learn, Unlearn and Relearn: An Online Learning Paradigm for Deep Neural Networks
Vijaya Raghavan T. Ramkumar, Elahe Arani, Bahram Zonooz
Deep neural networks (DNNs) are often trained on the premise that the complete training data set is provided ahead of time. However, in real-world scenarios, data often arrive in c…
Task-Aware Information Routing from Common Representation Space in Lifelong Learning
Prashant Bhat, Bahram Zonooz, Elahe Arani
Intelligent systems deployed in the real world suffer from catastrophic forgetting when exposed to a sequence of tasks. Humans, on the other hand, acquire, consolidate, and transfe…
Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Humans excel at lifelong learning, as the brain has evolved to be robust to distribution shifts and noise in our ever-changing environment. Deep neural networks (DNNs), however, ex…
Task Agnostic Representation Consolidation: a Self-supervised based Continual Learning Approach
Prashant Bhat, Bahram Zonooz, Elahe Arani
Continual learning (CL) over non-stationary data streams remains one of the long-standing challenges in deep neural networks (DNNs) as they are prone to catastrophic forgetting. CL…
Consistency is the key to further mitigating catastrophic forgetting in continual learning
Prashant Bhat, Bahram Zonooz, Elahe Arani
Deep neural networks struggle to continually learn multiple sequential tasks due to catastrophic forgetting of previously learned tasks. Rehearsal-based methods which explicitly st…
Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System
Elahe Arani, Fahad Sarfraz, Bahram Zonooz
Humans excel at continually learning from an ever-changing environment whereas it remains a challenge for deep neural networks which exhibit catastrophic forgetting. The complement…
Improving the Efficiency of Transformers for Resource-Constrained Devices
Hamid Tabani, Ajay Balasubramaniam, Shabbir Marzban +2
Transformers provide promising accuracy and have become popular and used in various domains such as natural language processing and computer vision. However, due to their massive n…