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Found 9 papers, 8 papers with code
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.
Learning Self-Supervised Representations for Label Efficient Cross-Domain Knowledge Transfer on Diabetic Retinopathy Fundus Images
Most of the existing DR image classification methods are based on supervised learning which requires a lot of time-consuming and expensive medical domain experts-annotated data for training.
Domain Adaptable Self-supervised Representation Learning on Remote Sensing Satellite Imagery
The proposed approach investigates the knowledge transfer of selfsupervised representations across the distinct source and target data distributions in depth in the remote sensing data domain.
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
Deep Neural Network approaches for Analysing Videos of Music Performances
While this idea was proposed in a previous study, this paper introduces several novelties: (i) Presents a novel method to overcome the class imbalance challenge and make learning possible for co-existent gestures by batch balancing approach and spatial-temporal representations of gestures.
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
