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Found 7 papers, 0 papers with code
A Large-scale Evaluation of Pretraining Paradigms for the Detection of Defects in Electroluminescence Solar Cell Images
Additionally, we provide a large-scale unlabelled EL image dataset of $22000$ images, and a $642$-image labelled semantic segmentation EL dataset, for further research in developing self- and semi-supervised training techniques in this domain.
DeepSet SimCLR: Self-supervised deep sets for improved pathology representation learning
Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures.
Affine transformation estimation improves visual self-supervised learning
The standard approach to modern self-supervised learning is to generate random views through data augmentations and minimise a loss computed from the representations of these views.
On the robustness of self-supervised representations for multi-view object classification
It is known that representations from self-supervised pre-training can perform on par, and often better, on various downstream tasks than representations from fully-supervised pre-training.
Explicit homography estimation improves contrastive self-supervised learning
We show how the inclusion of this module to regress the parameters of an affine transformation or homography, in addition to the original contrastive objective, improves both performance and learning speed.
Human Action Recognition using Local Two-Stream Convolution Neural Network Features and Support Vector Machines
This paper proposes a simple yet effective method for human action recognition in video.
SummaryNet: A Multi-Stage Deep Learning Model for Automatic Video Summarisation
Video summarisation can be posed as the task of extracting important parts of a video in order to create an informative summary of what occurred in the video.
