Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks
Explore this paper's citation graph
Summary
A simple semi-supervised learning approach for images based on in-painting using an adversarial loss is introduced, able to directly train large VGG-style networks in a semi- supervised fashion.
- Type
- preprint
- Published
- 2016-11-19
- Cited by
- 165
- References
- 41
- Access
- Open access
- OpenAlex
- https://openalex.org/W2556707115
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:10020949
Keywords
Discriminator, Pascal (unit), Computer science, Artificial intelligence, Adversarial system
References
- Unsupervised Visual Representation Learning by Context Prediction
- Stacked What-Where Auto-encoders
- Video (language) modeling: a baseline for generative models of natural videos
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- Auto-Encoding Variational Bayes
- Extracting and composing robust features with denoising autoencoders
- Semi-supervised Learning with Deep Generative Models
- Multi-Task Bayesian Optimization
- Conditional Generative Adversarial Nets
- A Fast Learning Algorithm for Deep Belief Nets
- Discriminative Unsupervised Feature Learning with Convolutional Neural Networks
- Distributed Representations of Words and Phrases and their Compositionality
- Learning with Local and Global Consistency
- ImageNet classification with deep convolutional neural networks
- Learning Deep Generative Models
- Character-level Convolutional Networks for Text Classification
- Deep multi-scale video prediction beyond mean square error
- Pixel Recurrent Neural Networks
- Learning to Generate Chairs, Tables and Cars with Convolutional Networks
- Context Encoders: Feature Learning by Inpainting
Cited by
- Generative adversarial networks for brain lesion detection
- Stabilizing GAN Training with Multiple Random Projections
- Channel-Recurrent Variational Autoencoders
- Multi-task Self-Supervised Visual Learning
- Part-of-Speech Tagging for Twitter with Adversarial Neural Networks
- Deep Growing Learning
- Multi-scale multi-class conditional generative adversarial network for handwritten character generation
- Instance Map Based Image Synthesis With a Denoising Generative Adversarial Network
- The state of fakery
- IntPhys 2019: A Benchmark for Visual Intuitive Physics Understanding
- Projection decomposition algorithm for dual-energy computed tomography via deep neural network
- Semisupervised and Weakly Supervised Road Detection Based on Generative Adversarial Networks
- Channel-Recurrent Autoencoding for Image Modeling
- Pedestrian-Synthesis-GAN: Generating Pedestrian Data in Real Scene and Beyond
- Self-Supervised Feature Learning by Learning to Spot Artifacts
- Few-shot Classifier GAN
- Task-oriented learning of structured probability distributions
- Semi-Supervised Learning with Uncertainty
- Vision-Based Defect Detection for Mobile Phone Cover Glass using Deep Neural Networks
- Learning Hierarchical Semantic Image Manipulation through Structured Representations