Publications (8)
Revisiting RCAN: Improved Training for Image Super-Resolution
Zudi Lin, Prateek Garg, Atmadeep Banerjee +6
Image super-resolution (SR) is a fast-moving field with novel architectures attracting the spotlight. However, most SR models were optimized with dated training strategies. In this…
InceptionXML: A Lightweight Framework with Synchronized Negative Sampling for Short Text Extreme Classification
Siddhant Kharbanda, Atmadeep Banerjee, Devaansh Gupta +2
Automatic annotation of short-text data to a large number of target labels, referred to as Short Text Extreme Classification, has found numerous applications including prediction o…
Reconstructing the Mind's Eye: fMRI-to-Image with Contrastive Learning and Diffusion Priors
Paul S. Scotti, Atmadeep Banerjee, Jimmie Goode +9
We present MindEye, a novel fMRI-to-image approach to retrieve and reconstruct viewed images from brain activity. Our model comprises two parallel submodules that are specialized f…
Meta-DRN: Meta-Learning for 1-Shot Image Segmentation
Atmadeep Banerjee
Modern deep learning models have revolutionized the field of computer vision. But, a significant drawback of most of these models is that they require a large number of labelled ex…
CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification
Siddhant Kharbanda, Atmadeep Banerjee, Erik Schultheis +1
Extreme Multi-label Text Classification (XMC) involves learning a classifier that can assign an input with a subset of most relevant labels from millions of label choices. Recent a…
MXR-U-Nets for Real Time Hyperspectral Reconstruction
Atmadeep Banerjee, Akash Palrecha
In recent times, CNNs have made significant contributions to applications in image generation, super-resolution and style transfer. In this paper, we build upon the work of Howard…