Semi-Supervised Image Classification Edit Task
Computer Vision • Image Classification
About
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:
- An overview of proxy-label approaches for semi-supervised learning - Sebastian Ruder
- Semi-Supervised Learning in Computer Vision - Amit Chaudhary
( Image credit: Self-Supervised Semi-Supervised Learning )
Benchmarks
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Datasets
Greatest papers with code
Improved Techniques for Training GANs
NeurIPS 2016
• tensorflow/models
• 
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
CONDITIONAL IMAGE GENERATION SEMI-SUPERVISED IMAGE CLASSIFICATION
Meta Pseudo Labels
• google-research/google-research
•
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.
Milking CowMask for Semi-Supervised Image Classification
• google-research/google-research
•
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%.
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
• eriklindernoren/PyTorch-GAN
• 
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss.
mixup: Beyond Empirical Risk Minimization
ICLR 2018
• rwightman/pytorch-image-models
•
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
Bootstrap your own latent: A new approach to self-supervised Learning
• deepmind/deepmind-research
•
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
REPRESENTATION LEARNING SELF-SUPERVISED IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING SEMI-SUPERVISED IMAGE CLASSIFICATION
Big Self-Supervised Models are Strong Semi-Supervised Learners
NeurIPS 2020
• google-research/simclr
•
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.
SELF-SUPERVISED IMAGE CLASSIFICATION SEMI-SUPERVISED IMAGE CLASSIFICATION
A Simple Framework for Contrastive Learning of Visual Representations
ICML 2020
• google-research/simclr
•
This paper presents SimCLR: a simple framework for contrastive learning of visual representations.
SELF-SUPERVISED IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING SEMI-SUPERVISED IMAGE CLASSIFICATION
Unsupervised Data Augmentation for Consistency Training
NeurIPS 2020
• google-research/uda
•
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
IMAGE AUGMENTATION SEMI-SUPERVISED IMAGE CLASSIFICATION TEXT CLASSIFICATION TRANSFER LEARNING
Self-supervised Pretraining of Visual Features in the Wild
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)
SELF-SUPERVISED IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING SEMI-SUPERVISED IMAGE CLASSIFICATION