66 citations · 71 across the 5 of their papers we have counts for
6 papers
Fortifying Fully Convolutional Generative Adversarial Networks for Image Super-Resolution Using Divergence Measures
Arkaprabha Basu, Kushal Bose, Sankha Subhra Mullick +2
Super-Resolution (SR) is a time-hallowed image processing problem that aims to improve the quality of a Low-Resolution (LR) sample up to the standard of its High-Resolution (HR) co…
Interval Bound Interpolation for Few-shot Learning with Few Tasks
Shounak Datta, Sankha Subhra Mullick, Anish Chakrabarty +1
Few-shot learning aims to transfer the knowledge acquired from training on a diverse set of tasks to unseen tasks from the same task distribution with a limited amount of labeled d…
Appropriateness of Performance Indices for Imbalanced Data Classification: An Analysis
Sankha Subhra Mullick, Shounak Datta, Sourish Gunesh Dhekane +1
Indices quantifying the performance of classifiers under class-imbalance, often suffer from distortions depending on the constitution of the test set or the class-specific classifi…
A Black-box Adversarial Attack Strategy with Adjustable Sparsity and Generalizability for Deep Image Classifiers
Arka Ghosh, Sankha Subhra Mullick, Shounak Datta +3
Constructing adversarial perturbations for deep neural networks is an important direction of research. Crafting image-dependent adversarial perturbations using white-box feedback h…
Generative Adversarial Minority Oversampling
Sankha Subhra Mullick, Shounak Datta, Swagatam Das
Class imbalance is a long-standing problem relevant to a number of real-world applications of deep learning. Oversampling techniques, which are effective for handling class imbalan…
Diversifying Support Vector Machines for Boosting using Kernel Perturbation: Applications to Class Imbalance and Small Disjuncts
Shounak Datta, Sayak Nag, Sankha Subhra Mullick +1
The diversification (generating slightly varying separating discriminators) of Support Vector Machines (SVMs) for boosting has proven to be a challenge due to the strong learning n…