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Semi-Supervised Image Classification

42 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 leaderboards:

( 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 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 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

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

13 Jun 2020deepmind/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.

REPRESENTATION LEARNING SELF-SUPERVISED IMAGE CLASSIFICATION SELF-SUPERVISED LEARNING SEMI-SUPERVISED IMAGE CLASSIFICATION

Unsupervised Data Augmentation for Consistency Training

29 Apr 2019google-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

Big Self-Supervised Models are Strong Semi-Supervised Learners

17 Jun 2020google-research/simclr

The proposed semi-supervised learning algorithm can be summarized in three steps: unsupervised pretraining of a big ResNet model using SimCLRv2 (a modification of SimCLR), 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

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