Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Fuente:
arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2016
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| _version_ | 1866914711795138560 |
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| author | Denton, Remi Gross, Sam Fergus, Rob |
| author_facet | Denton, Remi Gross, Sam Fergus, Rob |
| contents | We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_1611_06430 |
| institution | arXiv |
| publishDate | 2016 |
| record_format | arxiv |
| spellingShingle | Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks Denton, Remi Gross, Sam Fergus, Rob Computer Vision and Pattern Recognition We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods. |
| title | Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/1611.06430 |