activity
20182022
most citedMeta-Consolidation for Continual Learning

27 citations · 32 across the 5 of their papers we have counts for

collaborators

8 papers

cs.CV2022

Spacing Loss for Discovering Novel Categories

K J Joseph, Sujoy Paul, Gaurav Aggarwal +4

Novel Class Discovery (NCD) is a learning paradigm, where a machine learning model is tasked to semantically group instances from unlabeled data, by utilizing labeled instances fro…

cs.CV20223 cited

Energy-based Latent Aligner for Incremental Learning

K J Joseph, Salman Khan, Fahad Shahbaz Khan +2

Deep learning models tend to forget their earlier knowledge while incrementally learning new tasks. This behavior emerges because the parameter updates optimized for the new tasks…

cs.CV2021

Towards Open World Object Detection

K J Joseph, Salman Khan, Fahad Shahbaz Khan +1

Humans have a natural instinct to identify unknown object instances in their environments. The intrinsic curiosity about these unknown instances aids in learning about them, when t…

cs.CV202027 cited

Meta-Consolidation for Continual Learning

K J Joseph, Vineeth N Balasubramanian

The ability to continuously learn and adapt itself to new tasks, without losing grasp of already acquired knowledge is a hallmark of biological learning systems, which current deep…

cs.CV20201 cited

Zero Shot Domain Generalization

Udit Maniyar, Joseph K J, Aniket Anand Deshmukh +2

Standard supervised learning setting assumes that training data and test data come from the same distribution (domain). Domain generalization (DG) methods try to learn a model that…

cs.LG20191 cited

Submodular Batch Selection for Training Deep Neural Networks

K J Joseph, Vamshi Teja R, Krishnakant Singh +1

Mini-batch gradient descent based methods are the de facto algorithms for training neural network architectures today. We introduce a mini-batch selection strategy based on submodu…