Search Results for author: Deepesh Mehta

Found 2 papers, 0 papers with code

Balancing Discriminability and Transferability for Source-Free Domain Adaptation

no code implementations16 Jun 2022 Jogendra Nath Kundu, Akshay Kulkarni, Suvaansh Bhambri, Deepesh Mehta, Shreyas Kulkarni, Varun Jampani, R. Venkatesh Babu

Conventional domain adaptation (DA) techniques aim to improve domain transferability by learning domain-invariant representations; while concurrently preserving the task-discriminability knowledge gathered from the labeled source data.

Domain Adaptation Semantic Segmentation

GI-NNet \& RGI-NNet: Development of Robotic Grasp Pose Models, Trainable with Large as well as Limited Labelled Training Datasets, under supervised and semi supervised paradigms

no code implementations15 Jul 2021 Priya Shukla, Nilotpal Pramanik, Deepesh Mehta, G. C. Nandi

It is trained on Cornell Grasping Dataset (CGD) and attained 98. 87% grasp pose accuracy for detecting both regular and irregular shaped objects from RGB-Depth (RGB-D) images while requiring only one third of the network trainable parameters as compared to the existing approaches.

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