activity
20192024
most citedLearning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System

37 citations · 64 across the 29 of their papers we have counts for

collaborators
Showing 2023Show all

11 papers · 1 filter

cs.CV2023

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…

cs.CV2023

Dual Cognitive Architecture: Incorporating Biases and Multi-Memory Systems for Lifelong Learning

Shruthi Gowda, Bahram Zonooz, Elahe Arani

Artificial neural networks (ANNs) exhibit a narrow scope of expertise on stationary independent data. However, the data in the real world is continuous and dynamic, and ANNs must a…

cs.AI2023

TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion

Preetha Vijayan, Prashant Bhat, Elahe Arani +1

Continual learning (CL) has remained a persistent challenge for deep neural networks due to catastrophic forgetting (CF) of previously learned tasks. Several techniques such as wei…

cs.CV2023

Enhancing Performance of Vision Transformers on Small Datasets through Local Inductive Bias Incorporation

Ibrahim Batuhan Akkaya, Senthilkumar S. Kathiresan, Elahe Arani +1

Vision transformers (ViTs) achieve remarkable performance on large datasets, but tend to perform worse than convolutional neural networks (CNNs) when trained from scratch on smalle…

cs.CV20235 cited

BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning

Kishaan Jeeveswaran, Prashant Bhat, Bahram Zonooz +1

The ability of deep neural networks to continually learn and adapt to a sequence of tasks has remained challenging due to catastrophic forgetting of previously learned tasks. Human…

cs.LG2023

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…