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Semi-Supervised Image Classification

17 papers with code · Computer Vision

Semi-supervised image classification leverages unlabelled data as well as labelled data to increase classification performance.

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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

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

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

6 May 2019google-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

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.

DATA AUGMENTATION 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

Good Semi-supervised Learning that Requires a Bad GAN

NeurIPS 2017 kimiyoung/ssl_bad_gan

Semi-supervised learning methods based on generative adversarial networks (GANs) obtained strong empirical results, but it is not clear 1) how the discriminator benefits from joint training with a generator, and 2) why good semi-supervised classification performance and a good generator cannot be obtained at the same time.

SEMI-SUPERVISED IMAGE CLASSIFICATION