62 citations · 153 across the 45 of their papers we have counts for
14 papers · 1 filter
Transformers in Unsupervised Structure-from-Motion
Hemang Chawla, Arnav Varma, Elahe Arani +1
Transformers have revolutionized deep learning based computer vision with improved performance as well as robustness to natural corruptions and adversarial attacks. Transformers ar…
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…
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…
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…
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…
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…
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…
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…
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…
LSFSL: Leveraging Shape Information in Few-shot Learning
Deepan Chakravarthi Padmanabhan, Shruthi Gowda, Elahe Arani +1
Few-shot learning (FSL) techniques seek to learn the underlying patterns in data using fewer samples, analogous to how humans learn from limited experience. In this limited-data sc…
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…
Dynamically Modular and Sparse General Continual Learning
Arnav Varma, Elahe Arani, Bahram Zonooz
Real-world applications often require learning continuously from a stream of data under ever-changing conditions. When trying to learn from such non-stationary data, deep neural ne…