10 papers
Attaining Class-level Forgetting in Pretrained Model using Few Samples
Pravendra Singh, Pratik Mazumder, Mohammed Asad Karim
In order to address real-world problems, deep learning models are jointly trained on many classes. However, in the future, some classes may become restricted due to privacy/ethical…
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…
RNNP: A Robust Few-Shot Learning Approach
Pratik Mazumder, Pravendra Singh, Vinay P. Namboodiri
Learning from a few examples is an important practical aspect of training classifiers. Various works have examined this aspect quite well. However, all existing approaches assume t…
Passive Batch Injection Training Technique: Boosting Network Performance by Injecting Mini-Batches from a different Data Distribution
Pravendra Singh, Pratik Mazumder, Vinay P. Namboodiri
This work presents a novel training technique for deep neural networks that makes use of additional data from a distribution that is different from that of the original input data.…