9 citations · 12 across the 12 of their papers we have counts for
4 papers · 1 filter
DILF-EN framework for Class-Incremental Learning
Mohammed Asad Karim, Indu Joshi, Pratik Mazumder +1
Deep learning models suffer from catastrophic forgetting of the classes in the older phases as they get trained on the classes introduced in the new phase in the class-incremental…
Fair Visual Recognition in Limited Data Regime using Self-Supervision and Self-Distillation
Pratik Mazumder, Pravendra Singh, Vinay P. Namboodiri
Deep learning models generally learn the biases present in the training data. Researchers have proposed several approaches to mitigate such biases and make the model fair. Bias mit…
Rectification-based Knowledge Retention for Continual Learning
Pravendra Singh, Pratik Mazumder, Piyush Rai +1
Deep learning models suffer from catastrophic forgetting when trained in an incremental learning setting. In this work, we propose a novel approach to address the task incremental…
Few-Shot Lifelong Learning
Pratik Mazumder, Pravendra Singh, Piyush Rai
Many real-world classification problems often have classes with very few labeled training samples. Moreover, all possible classes may not be initially available for training, and m…