62 citations · 153 across the 45 of their papers we have counts for
46 papers
Nanoscale chemical engineering of Ti3C2Tx MXene multilayers through in situ electron beam irradiation in a TEM
Ayoub Benmoumen, Marie-Laure David, Noé Condé +7
Defect engineering in low-dimensional materials is a powerful approach to tailor their structure and chemistry at the atomic scale, with high relevance for tuning physical properti…
Foveation-Guided Dynamic Token Selection for Robust and Efficient Vision Transformers
Ibrahim Batuhan Akkaya, Kishaan Jeeveswaran, Bahram Zonooz +1
The human visual system (HVS) employs foveated sampling and eye movements to achieve efficient perception, conserving both metabolic energy and computational resources. Drawing ins…
PhysVid: Physics Aware Local Conditioning for Generative Video Models
Saurabh Pathak, Elahe Arani, Mykola Pechenizkiy +1
Generative video models achieve high visual fidelity but often violate basic physical principles, limiting reliability in real-world settings. Prior attempts to inject physics rely…
Depth3DLane: Fusing Monocular 3D Lane Detection with Self-Supervised Monocular Depth Estimation
Max van den Hoven, Kishaan Jeeveswaran, Pieter Piscaer +3
Monocular 3D lane detection is essential for autonomous driving, but challenging due to the inherent lack of explicit spatial information. Multi-modal approaches rely on expensive…
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…
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…
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…