Search Results for author:
Found 10 papers, 6 papers with code
Breast Cancer Histopathology Image Classification and Localization using Multiple Instance Learning
We aim to provide a better interpretation of classification results by providing localization on microscopic histopathology images.
Multi-task Hierarchical Adversarial Inverse Reinforcement Learning
Multi-task Imitation Learning (MIL) aims to train a policy capable of performing a distribution of tasks based on multi-task expert demonstrations, which is essential for general-purpose robots.
Domain Adaptive Few-Shot Open-Set Learning
Few-shot learning has made impressive strides in addressing the crucial challenges of recognizing unknown samples from novel classes in target query sets and managing visual shifts between domains.
Ranked #1 on
cross-domain few-shot learning
on Office-Home
Reinforced Sequential Decision-Making for Sepsis Treatment: The POSNEGDM Framework with Mortality Classifier and Transformer
Sepsis, a life-threatening condition triggered by the body's exaggerated response to infection, demands urgent intervention to prevent severe complications.
Explaining decision of model from its prediction
This document summarizes different visual explanations methods such as CAM, Grad-CAM, Localization using Multiple Instance Learning - Saliency-based methods, Saliency-driven Class-Impressions, Muting pixels in input image - Adversarial methods and Activation visualization, Convolution filter visualization - Feature-based methods.
RSINet: Inpainting Remotely Sensed Images Using Triple GAN Framework
We tackle the problem of image inpainting in the remote sensing domain.
Image-based phenotyping of diverse Rice (Oryza Sativa L.) Genotypes
The present investigation is carried out for discriminating drought tolerant, and susceptible genotypes.
Saliency-driven Class Impressions for Feature Visualization of Deep Neural Networks
Existing visualization methods develop high confidence images consisting of both background and foreground features.
Multi-Source Open-Set Deep Adversarial Domain Adaptation
Besides, an adversarial learning strategy is followed to model the discriminator between the target-domain known and unknown classes.
Understanding the Natural Language of DNA using Encoder-Decoder Foundation Models with Byte-level Precision
This paper presents the Ensemble Nucleotide Byte-level Encoder-Decoder (ENBED) foundation model, analyzing DNA sequences at byte-level precision with an encoder-decoder Transformer architecture.
