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08/23/2023
Less is More – Towards parsimonious multi-task models using structured sparsity
Group sparsity in Machine Learning (ML) encourages simpler, more interpr...
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06/19/2023
Performance of data-driven inner speech decoding with same-task EEG-fMRI data fusion and bimodal models
Decoding inner speech from the brain signal via hybridisation of fMRI an...
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04/27/2023
Robust and Fast Vehicle Detection using Augmented Confidence Map
Vehicle detection in real-time scenarios is challenging because of the t...
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02/08/2023
A Systematic Performance Analysis of Deep Perceptual Loss Networks Breaks Transfer Learning Conventions
Deep perceptual loss is a type of loss function in computer vision that ...
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10/18/2022
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
This work presents a novel self-supervised representation learning metho...
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10/13/2022
Multi-Task Meta Learning: learn how to adapt to unseen tasks
This work aims to integrate two learning paradigms Multi-Task Learning (...
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03/15/2022
Magnification Prior: A Self-Supervised Method for Learning Representations on Breast Cancer Histopathological Images
This work presents a novel self-supervised pre-training method to learn ...
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11/23/2021
Sharing to learn and learning to share - Fitting together Meta-Learning, Multi-Task Learning, and Transfer Learning : A meta review
Integrating knowledge across different domains is an essential feature o...
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03/08/2019
ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records
We propose a Historical Document Reading Challenge on Large Chinese Stru...
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12/05/2017
