Self-Supervised Image Classification
85 papers with code • 2 benchmarks • 1 datasets
This is the task of image classification using representations learnt with self-supervised learning. Self-supervised methods generally involve a pretext task that is solved to learn a good representation and a loss function to learn with. One example of a loss function is an autoencoder based loss where the goal is reconstruction of an image pixel-by-pixel. A more popular recent example is a contrastive loss, which measure the similarity of sample pairs in a representation space, and where there can be a varying target instead of a fixed target to reconstruct (as in the case of autoencoders).
A common evaluation protocol is to train a linear classifier on top of (frozen) representations learnt by self-supervised methods. The leaderboards for the linear evaluation protocol can be found below. In practice, it is more common to fine-tune features on a downstream task. An alternative evaluation protocol therefore uses semi-supervised learning and finetunes on a % of the labels. The leaderboards for the finetuning protocol can be accessed here.
You may want to read some blog posts before reading the papers and checking the leaderboards:
- Contrastive Self-Supervised Learning - Ankesh Anand
- The Illustrated Self-Supervised Learning - Amit Chaudhary
- Self-supervised learning and computer vision - Jeremy Howard
- Self-Supervised Representation Learning - Lilian Weng
There is also Yann LeCun's talk at AAAI-20 which you can watch here (35:00+).
( Image credit: A Simple Framework for Contrastive Learning of Visual Representations )
Libraries
Use these libraries to find Self-Supervised Image Classification models and implementationsLatest papers with no code
Seed the Views: Hierarchical Semantic Alignment for Contrastive Representation Learning
In this paper, we propose a hierarchical semantic alignment strategy via expanding the views generated by a single image to \textbf{Cross-samples and Multi-level} representation, and models the invariance to semantically similar images in a hierarchical way.
A comparative study of semi- and self-supervised semantic segmentation of biomedical microscopy data
In recent years, Convolutional Neural Networks (CNNs) have become the state-of-the-art method for biomedical image analysis.
Consensus Clustering With Unsupervised Representation Learning
Recent advances in deep clustering and unsupervised representation learning are based on the idea that different views of an input image (generated through data augmentation techniques) must either be closer in the representation space, or have a similar cluster assignment.
Self-supervised Visual Feature Learning with Deep Neural Networks: A Survey
This paper provides an extensive review of deep learning-based self-supervised general visual feature learning methods from images or videos.