# Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1611.06430 · HTML: https://mlsanity.com/paper/1611.06430/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2016.

## Built on by (most cited first, 19 of 19)

- [A Survey on Deep Semi-supervised Learning](https://mlsanity.com/paper/2103.00550/) (2021)
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- [Revisiting Consistency Regularization for Semi-Supervised Learning](https://mlsanity.com/paper/2112.05825/) (2021)
- [A review of Generative Adversarial Networks (GANs) and its applications in a wide variety of disciplines -- From Medical to Remote Sensing](https://mlsanity.com/paper/2110.01442/) (2021)
- [Reconstruction-Aware Prior Distillation for Semi-supervised Point Cloud Completion](https://mlsanity.com/paper/2204.09186/) (2022)
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- [Boosting Semi-Supervised Scene Text Recognition via Viewing and Summarizing](https://mlsanity.com/paper/2411.15585/) (2024)
- [ReRankMatch: Semi-Supervised Learning with Semantics-Oriented Similarity Representation](https://mlsanity.com/paper/2102.06328/) (2021)
- [Deep Dive into Semi-Supervised ELBO for Improving Classification Performance](https://mlsanity.com/paper/2108.12734/) (2021)
- [RankingMatch: Delving into Semi-Supervised Learning with Consistency Regularization and Ranking Loss](https://mlsanity.com/paper/2110.04430/) (2021)
- [Gradient Imitation Reinforcement Learning for General Low-Resource Information Extraction](https://mlsanity.com/paper/2211.06014/) (2022)
- [Happy People -- Image Synthesis as Black-Box Optimization Problem in the Discrete Latent Space of Deep Generative Models](https://mlsanity.com/paper/2306.06684/) (2023)
- [Robust Loss Functions for Object Grasping under Limited Ground Truth](https://mlsanity.com/paper/2409.05742/) (2024)
- [SIT-FER: Integration of Semantic-, Instance-, Text-level Information for Semi-supervised Facial Expression Recognition](https://mlsanity.com/paper/2503.18463/) (2025)
- [DiffuMural: Restoring Dunhuang Murals with Multi-scale Diffusion](https://mlsanity.com/paper/2504.09513/) (2025)
- [Uncertainty-Guided Edge Learning for Deep Image Regression in Remote Sensing](https://mlsanity.com/paper/2605.05590/) (2026)

## Abstract (arXiv)

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.