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Semi-Supervised Image Classification

37 papers with code · Computer Vision

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 leaderboard:

( Image credit: Self-Supervised Semi-Supervised Learning )

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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.

CONDITIONAL IMAGE GENERATION SEMI-SUPERVISED IMAGE CLASSIFICATION

Milking CowMask for Semi-Supervised Image Classification

26 Mar 2020google-research/google-research

Using CowMask as the augmentation method in semi-supervised consistency regularization, we establish a new 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 IMAGE CLASSIFICATION

Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

19 Nov 2016eriklindernoren/Keras-GAN

We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss.

SEMI-SUPERVISED IMAGE CLASSIFICATION

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.

DOMAIN GENERALIZATION SEMI-SUPERVISED IMAGE CLASSIFICATION

Unsupervised Data Augmentation for Consistency Training

ICLR 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.

IMAGE AUGMENTATION SEMI-SUPERVISED IMAGE CLASSIFICATION TEXT CLASSIFICATION TRANSFER LEARNING

Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results

NeurIPS 2017 CuriousAI/mean-teacher

Without changing the network architecture, Mean Teacher achieves an error rate of 4. 35% on SVHN with 250 labels, outperforming Temporal Ensembling trained with 1000 labels.

SEMI-SUPERVISED IMAGE CLASSIFICATION

MixMatch: A Holistic Approach to Semi-Supervised Learning

NeurIPS 2019 google-research/mixmatch

Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.

SEMI-SUPERVISED IMAGE CLASSIFICATION