Semi-Supervised Image Classification
99 papers with code • 37 benchmarks • 12 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:
- 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
These leaderboards are used to track progress in Semi-Supervised Image Classification
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Libraries
Use these libraries to find Semi-Supervised Image Classification models and implementationsDatasets
CIFAR-10
ImageNet
CIFAR-100
SVHN
STL-10
Caltech-101
Caltech-256
Salinas
Semi-iNat
HeriGraph
Most implemented papers
mixup: Beyond Empirical Risk Minimization
facebookresearch/mixup-cifar10
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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.
A Simple Framework for Contrastive Learning of Visual Representations
google-research/simclr
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ICML 2020
This paper presents SimCLR: a simple framework for contrastive learning of visual representations.
Improved Techniques for Training GANs
openai/improved-gan
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NeurIPS 2016
We present a variety of new architectural features and training procedures that we apply to the generative adversarial networks (GANs) framework.
MixMatch: A Holistic Approach to Semi-Supervised Learning
google-research/mixmatch
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NeurIPS 2019
Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets.
Bootstrap your own latent: A new approach to self-supervised Learning
deepmind/deepmind-research
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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.
Improved Regularization of Convolutional Neural Networks with Cutout
Convolutional neural networks are capable of learning powerful representational spaces, which are necessary for tackling complex learning tasks.
Representation Learning with Contrastive Predictive Coding
davidtellez/contrastive-predictive-coding
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The key insight of our model is to learn such representations by predicting the future in latent space by using powerful autoregressive models.
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
google-research/fixmatch
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NeurIPS 2020
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.
Learning Transferable Visual Models From Natural Language Supervision
openai/CLIP
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State-of-the-art computer vision systems are trained to predict a fixed set of predetermined object categories.
Unsupervised Data Augmentation for Consistency Training
google-research/uda
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NeurIPS 2020
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.
