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Semi-Supervised Image Classification

24 papers with code · Computer Vision

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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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

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.

SEMI-SUPERVISED IMAGE CLASSIFICATION

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.

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

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

Improved Regularization of Convolutional Neural Networks with Cutout

15 Aug 2017uoguelph-mlrg/Cutout

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

IMAGE AUGMENTATION SEMI-SUPERVISED IMAGE CLASSIFICATION