2 citations · 4 across the 4 of their papers we have counts for
4 papers
Perturbing the Gradient for Alleviating Meta Overfitting
Manas Gogoi, Sambhavi Tiwari, Shekhar Verma
The reason for Meta Overfitting can be attributed to two factors: Mutual Non-exclusivity and the Lack of diversity, consequent to which a single global function can fit the support…
Learning to Learn with Indispensable Connections
Sambhavi Tiwari, Manas Gogoi, Shekhar Verma +1
Meta-learning aims to solve unseen tasks with few labelled instances. Nevertheless, despite its effectiveness for quick learning in existing optimization-based methods, it has seve…
Adaptive Prototypical Networks
Manas Gogoi, Sambhavi Tiwari, Shekhar Verma
Prototypical network for Few shot learning tries to learn an embedding function in the encoder that embeds images with similar features close to one another in the embedding space.…
MAC: A Meta-Learning Approach for Feature Learning and Recombination
S. Tiwari, M. Gogoi, S. Verma +1
Optimization-based meta-learning aims to learn an initialization so that a new unseen task can be learned within a few gradient updates. Model Agnostic Meta-Learning (MAML) is a be…