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Found 15 papers, 8 papers with code
Pedagogical Principles in the Online Teaching of Text Mining: A Retrospection
The ongoing COVID-19 pandemic has brought online education to the forefront of pedagogical discussions.
Vehicle Detection Performance in Nordic Region
This paper addresses the critical challenge of vehicle detection in the harsh winter conditions in the Nordic regions, characterized by heavy snowfall, reduced visibility, and low lighting.
Less is More -- Towards parsimonious multi-task models using structured sparsity
In this work, we introduce channel-wise l1/l2 group sparsity in the shared convolutional layers parameters (or weights) of the multi-task learning model.
Can Self-Supervised Representation Learning Methods Withstand Distribution Shifts and Corruptions?
Detailed experiments have been conducted to study the robustness of self-supervised learning methods on distribution shifts and image corruptions.
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 and EEG data is explored to investigate the performance benefits over unimodal models.
Robust and Fast Vehicle Detection using Augmented Confidence Map
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, etc.
Functional Knowledge Transfer with Self-supervised Representation Learning
This work investigates the unexplored usability of self-supervised representation learning in the direction of functional knowledge transfer.
A Systematic Performance Analysis of Deep Perceptual Loss Networks: Breaking Transfer Learning Conventions
Deep perceptual loss is a type of loss function in computer vision that aims to mimic human perception by using the deep features extracted from neural networks.
Depth Contrast: Self-Supervised Pretraining on 3DPM Images for Mining Material Classification
This work presents a novel self-supervised representation learning method to learn efficient representations without labels on images from a 3DPM sensor (3-Dimensional Particle Measurement; estimates the particle size distribution of material) utilizing RGB images and depth maps of mining material on the conveyor belt.
Multi-Task Meta Learning: learn how to adapt to unseen tasks
In particular, it focuses simultaneous learning of multiple tasks, an element of MTL and promptly adapting to new tasks, a quality of meta learning.
Ranked #93 on
Semantic Segmentation
on NYU Depth v2
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 efficient representations without labels on histopathology medical images utilizing magnification factors.
Ranked #1 on
Breast Cancer Histology Image Classification
on BreakHis
(Accuracy (Inter-Patient) metric)
Breast Cancer Histology Image Classification (20% labels)
Classification Of Breast Cancer Histology Images
+2
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 of human learning.
TheNorth at SemEval-2020 Task 12: Hate Speech Detection Using RoBERTa
Hate speech detection on social media platforms is crucial as it helps to avoid severe situations, and severe harm to marginalized people and groups.
ICDAR 2019 Historical Document Reading Challenge on Large Structured Chinese Family Records
We propose a Historical Document Reading Challenge on Large Chinese Structured Family Records, in short ICDAR2019 HDRC CHINESE.
Recognizing Gender from Human Facial Regions using Genetic Algorithm
Next, a face image is segmented into facial regions using landmark points and features are extracted from each region.
