5 citations · 8 across the 4 of their papers we have counts for
5 papers
Generalized Cross-domain Multi-label Few-shot Learning for Chest X-rays
Aroof Aimen, Arsh Verma, Makarand Tapaswi +1
Real-world application of chest X-ray abnormality classification requires dealing with several challenges: (i) limited training data; (ii) training and evaluation sets that are der…
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…
On Duality Gap as a Measure for Monitoring GAN Training
Sahil Sidheekh, Aroof Aimen, Vineet Madan +1
Generative adversarial network (GAN) is among the most popular deep learning models for learning complex data distributions. However, training a GAN is known to be a challenging ta…