Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.
You may want to read some blog posts to get an overview before reading the papers and checking the leaderboards:
( Image credit: Self-Supervised Semi-Supervised Learning )
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We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
Ranked #11 on Conditional Image Generation on CIFAR-10
We present Meta Pseudo Labels, a semi-supervised learning method that achieves a new state-of-the-art top-1 accuracy of 90. 2% on ImageNet, which is 1. 6% better than the existing state-of-the-art.
Using it to provide perturbations for semi-supervised consistency regularization, we achieve a state-of-the-art result on ImageNet with 10% labeled data, with a top-5 error of 8. 76% and top-1 error of 26. 06%.
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss.
We also find that mixup reduces the memorization of corrupt labels, increases the robustness to adversarial examples, and stabilizes the training of generative adversarial networks.
Ranked #3 on Domain Generalization on ImageNet-A
From an augmented view of an image, we train the online network to predict the target network representation of the same image under a different augmented view.
Ranked #3 on Self-Supervised Image Classification on ImageNet
The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2, supervised fine-tuning on a few labeled examples, and distillation with unlabeled examples for refining and transferring the task-specific knowledge.
This paper presents SimCLR: a simple framework for contrastive learning of visual representations.
In this work, we present a new perspective on how to effectively noise unlabeled examples and argue that the quality of noising, specifically those produced by advanced data augmentation methods, plays a crucial role in semi-supervised learning.
Ranked #1 on Sentiment Analysis on Amazon Review Full
Recently, self-supervised learning methods like MoCo, SimCLR, BYOL and SwAV have reduced the gap with supervised methods.
Ranked #1 on Self-Supervised Image Classification on ImageNet (finetuned) (using extra training data)