Semi-Supervised Image Classification

65 papers with code • 34 benchmarks • 9 datasets

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 )

Greatest papers with code

Improved Techniques for Training GANs

tensorflow/models NeurIPS 2016

We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.

Conditional Image Generation Semi-Supervised Image Classification

Milking CowMask for Semi-Supervised Image Classification

google-research/google-research 26 Mar 2020

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%.

General Classification Semi-Supervised Image Classification

Meta Pseudo Labels

google-research/google-research CVPR 2021

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.

Meta-Learning Semi-Supervised Image Classification

mixup: Beyond Empirical Risk Minimization

rwightman/pytorch-image-models ICLR 2018

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.

Domain Generalization Semi-Supervised Image Classification

Bootstrap your own latent: A new approach to self-supervised Learning

deepmind/deepmind-research 13 Jun 2020

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.

Representation Learning Self-Supervised Image Classification +2

Big Self-Supervised Models are Strong Semi-Supervised Learners

google-research/simclr NeurIPS 2020

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

Improved Regularization of Convolutional Neural Networks with Cutout

PaddlePaddle/PaddleClas 15 Aug 2017

Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.

Domain Generalization Image Augmentation +1

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

facebookresearch/vissl 4 Mar 2021

This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors.

General Classification Object Detection +3