Semi-Supervised Image Classification
Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.
( 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.
#8 best model for
Conditional Image Generation
on CIFAR-10
CONDITIONAL IMAGE GENERATION SEMI-SUPERVISED IMAGE CLASSIFICATION
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss.
#5 best model for
Semi-Supervised Image Classification
on STL-10, 1000 Labels
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.
#7 best model for
Semi-Supervised Image Classification
on SVHN, 250 Labels
Unsupervised Data Augmentation for Consistency Training
arXiv 2019
• 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.
#4 best model for
Semi-Supervised Image Classification
on SVHN, 250 Labels
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.
#2 best model for
Image Classification
on STL-10
Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
• tensorflow/neural-structured-learning
•
In our experiments, we applied VAT to supervised and semi-supervised learning tasks on multiple benchmark datasets.
#5 best model for
Semi-Supervised Image Classification
on cifar10, 250 Labels
Semi-Supervised Learning with Ladder Networks
We combine supervised learning with unsupervised learning in deep neural networks.
#15 best model for
Semi-Supervised Image Classification
on CIFAR-10, 4000 Labels
Invariant Information Clustering for Unsupervised Image Classification and Segmentation
arXiv 2019
• xu-ji/IIC
•
The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.
SEMI-SUPERVISED IMAGE CLASSIFICATION UNSUPERVISED IMAGE CLASSIFICATION UNSUPERVISED SEMANTIC SEGMENTATION
FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
Semi-supervised learning (SSL) provides an effective means of leveraging unlabeled data to improve a model's performance.