5 citations · 6 across the 2 of their papers we have counts for
3 papers · 1 filter
Task Attended Meta-Learning for Few-Shot Learning
Aroof Aimen, Sahil Sidheekh, Narayanan C. Krishnan
Meta-learning (ML) has emerged as a promising direction in learning models under constrained resource settings like few-shot learning. The popular approaches for ML either learn a…
On Characterizing GAN Convergence Through Proximal Duality Gap
Sahil Sidheekh, Aroof Aimen, Narayanan C. Krishnan
Despite the accomplishments of Generative Adversarial Networks (GANs) in modeling data distributions, training them remains a challenging task. A contributing factor to this diffic…
Stress Testing of Meta-learning Approaches for Few-shot Learning
Aroof Aimen, Sahil Sidheekh, Vineet Madan +1
Meta-learning (ML) has emerged as a promising learning method under resource constraints such as few-shot learning. ML approaches typically propose a methodology to learn generaliz…