37 citations · 39 across the 8 of their papers we have counts for
11 papers
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…
Towards Brain Inspired Design for Addressing the Shortcomings of ANNs
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
As our understanding of the mechanisms of brain function is enhanced, the value of insights gained from neuroscience to the development of AI algorithms deserves further considerat…
A Study of Biologically Plausible Neural Network: The Role and Interactions of Brain-Inspired Mechanisms in Continual Learning
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Humans excel at continually acquiring, consolidating, and retaining information from an ever-changing environment, whereas artificial neural networks (ANNs) exhibit catastrophic fo…
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…
Sparse Coding in a Dual Memory System for Lifelong Learning
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Efficient continual learning in humans is enabled by a rich set of neurophysiological mechanisms and interactions between multiple memory systems. The brain efficiently encodes inf…
SYNERgy between SYNaptic consolidation and Experience Replay for general continual learning
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Continual learning (CL) in the brain is facilitated by a complex set of mechanisms. This includes the interplay of multiple memory systems for consolidating information as posited…
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…
Noisy Concurrent Training for Efficient Learning under Label Noise
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Deep neural networks (DNNs) fail to learn effectively under label noise and have been shown to memorize random labels which affect their generalization performance. We consider lea…
Adversarial Concurrent Training: Optimizing Robustness and Accuracy Trade-off of Deep Neural Networks
Elahe Arani, Fahad Sarfraz, Bahram Zonooz
Adversarial training has been proven to be an effective technique for improving the adversarial robustness of models. However, there seems to be an inherent trade-off between optim…
Knowledge Distillation Beyond Model Compression
Fahad Sarfraz, Elahe Arani, Bahram Zonooz
Knowledge distillation (KD) is commonly deemed as an effective model compression technique in which a compact model (student) is trained under the supervision of a larger pretraine…
Noise as a Resource for Learning in Knowledge Distillation
Elahe Arani, Fahad Sarfraz, Bahram Zonooz
While noise is commonly considered a nuisance in computing systems, a number of studies in neuroscience have shown several benefits of noise in the nervous system from enabling the…