Papers › Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Emily Denton, Sam Gross, Rob Fergus
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.
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46da871ca5fe3c51 · report
Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | STL-10 | CC-GAN² | Percentage correct | 77.8 | #69 of 117 | Archive leaderboard | report |
| Semi-Supervised Image Classification | STL-10, 1000 Labels | CC-GAN² | Accuracy | 77.80 | #11 of 13 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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