4 citations · 9 across the 16 of their papers we have counts for
6 papers · 1 filter
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?
Prakash Chandra Chhipa, Johan Rodahl Holmgren, Kanjar De +2
Self-supervised learning in computer vision aims to leverage the inherent structure and relationships within data to learn meaningful representations without explicit human annotat…
Less is More -- Towards parsimonious multi-task models using structured sparsity
Richa Upadhyay, Ronald Phlypo, Rajkumar Saini +1
Model sparsification in deep learning promotes simpler, more interpretable models with fewer parameters. This not only reduces the model's memory footprint and computational needs…
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models
Holly Wilson, Scott Wellington, Foteini Simistira Liwicki +13
Decoding inner speech from the brain signal via hybridisation of fMRI and EEG data is explored to investigate the performance benefits over unimodal models. Two different bimodal f…
Robust and Fast Vehicle Detection using Augmented Confidence Map
Hamam Mokayed, Palaiahnakote Shivakumara, Lama Alkhaled +4
Vehicle detection in real-time scenarios is challenging because of the time constraints and the presence of multiple types of vehicles with different speeds, shapes, structures, et…
Functional Knowledge Transfer with Self-supervised Representation Learning
Prakash Chandra Chhipa, Muskaan Chopra, Gopal Mengi +7
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer. In this work, functional knowledge tra…
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Gustav Grund Pihlgren, Konstantina Nikolaidou, Prakash Chandra Chhipa +4
In recent years, deep perceptual loss has been widely and successfully used to train machine learning models for many computer vision tasks, including image synthesis, segmentation…