Blog
The latest from Google Research
Reducing the Need for Labeled Data in Generative Adversarial Networks
miércoles, 20 de marzo de 2019
Posted by Mario Lučić, Research Scientist and Marvin Ritter, Software Engineer, Google AI Zürich
Generative adversarial networks
(GANs) are a powerful class of deep generative models.The main idea behind
GANs
is to train two neural networks:
the generator,
which learns how to synthesise data (such as an image), and the
discriminator,
which learns how to distinguish real data from the ones synthesised by the generator. This approach has been successfully used for
high-fidelity natural image synthesis
,
improving learned image compression
, data augmentation, and more.
Evolution of the generated samples as training progresses on ImageNet. The generator network is conditioned on the class (e.g., "great gray owl" or "golden retriever").
For natural image synthesis, state-of-the-art results are achieved by
conditional GANs
that, unlike unconditional GANs, use labels (e.g. car, dog, etc.) during training. While this makes the task easier and leads to significant improvements, this approach requires a large amount of labeled data that is rarely available in practice.
In "
High-Fidelity Image Generation With Fewer Labels
", we propose a new approach to reduce the amount of labeled data required to train state-of-the-art conditional GANs. When combined with recent advancements on large-scale GANs, we match the state-of-the-art in high-fidelity natural image synthesis using
10x fewer labels
. Based on this research, we are also releasing a major update to the
Compare GAN library
, which contains all the components necessary to train and evaluate modern GANs.
Improvements via Semi-supervision and Self-supervision
In conditional GANs, both the generator and discriminator are typically conditioned on class labels. In this work, we propose to replace the hand-annotated ground truth labels with inferred ones. To infer high-quality labels for a large dataset of mostly unlabeled data, we take a two-step approach: First, we learn a feature representation using only the unlabeled portion of the dataset. To learn the feature representations we make use of
self-supervision
in the form of a
recently introduced
approach, in which the unlabeled images are randomly rotated and a deep convolutional neural network is tasked with predicting the rotation angle. The idea is that the models need to be able to recognize the main objects and their shapes in order to be successful on this task.
An unlabeled image is randomly rotated and the network is tasked with predicting the rotation angle. Successful models need to capture semantically meaningful image features which can then be used for other vision tasks.
We then consider the activation pattern of one of the intermediate layers of the trained network as the new feature representation of the input, and train a classifier to recognize the label of that input using the labeled portion of the original data set. As the network was pre-trained to extract semantically meaningful features from the data (on the rotation prediction task), training this classifier is more sample-efficient than training the entire network from scratch. Finally, we use this classifier to label the unlabeled data.
To further improve the model quality and training stability we encourage the discriminator network to learn meaningful feature representations which are not forgotten during training by means of an auxiliary loss we introduced
previously
. These two advancements, combined with large-scale training lead to state-of-the-art conditional GANs for the task of ImageNet synthesis as measured by the
Fréchet Inception Distance
.
Given a latent vector the generator network produces an image. In each row, linear interpolation between the latent codes of the leftmost and the rightmost image results in a semantic interpolation in the image space.
Compare GAN: A Library for Training and Evaluating GANs
Cutting-edge research on GANs is heavily dependent on a well-engineered and well-tested codebase, since even replicating prior results and techniques requires a significant effort. In order to foster open science and allow the research community benefit from recent advancements, we are releasing a major update of the
Compare GAN
library. The library includes loss functions, regularization and normalization schemes, neural architectures, and quantitative metrics commonly used in modern GANs, and now supports:
Training on GPUs and TPUs.
Lightweight configuration via
Gin
(
examples
).
A plethora of data sets via the
TensorFlow datasets
library.
Conclusions and Future Work
Given the growing gap between labeled and unlabeled data sources, it is becoming
increasingly important
to be able to learn from only partially labeled data. We have shown that a simple yet powerful combination of self-supervision and semi-supervision can help to close this gap for GANs. We believe that self-supervision is a powerful idea that should be investigated for other generative modeling tasks.
Acknowledgments
Work conducted in collaboration with colleagues on the Google Brain team in Zürich, ETH Zürich and UCLA. We would like to thank our paper co-authors Michael Tschannen, Xiaohua Zhai, Olivier Bachem and Sylvain Gelly for their input and feedback. We would like to thank Alexander Kolesnikov, Lucas Beyer and Avital Oliver for helpful discussion on
self-supervised learning
and semi-supervised learning. We would like to thank Karol Kurach and Marcin Michalski for their major contributions to the Compare GAN library. We would also like to thank Andy Brock, Jeff Donahue and Karen Simonyan for their insights into training GANs on TPUs. The work described in this post also builds upon our work on “
Self-Supervised Generative Adversarial Networks
” with Ting Chen and Neil Houlsby.
Etiquetas
accessibility
ACL
ACM
Acoustic Modeling
Adaptive Data Analysis
ads
adsense
adwords
Africa
AI
AI for Social Good
Algorithms
Android
Android Wear
API
App Engine
App Inventor
April Fools
Art
Audio
Augmented Reality
Australia
Automatic Speech Recognition
AutoML
Awards
BigQuery
Cantonese
Chemistry
China
Chrome
Cloud Computing
Collaboration
Compression
Computational Imaging
Computational Photography
Computer Science
Computer Vision
conference
conferences
Conservation
correlate
Course Builder
crowd-sourcing
CVPR
Data Center
Data Discovery
data science
datasets
Deep Learning
DeepDream
DeepMind
distributed systems
Diversity
Earth Engine
economics
Education
Electronic Commerce and Algorithms
electronics
EMEA
EMNLP
Encryption
entities
Entity Salience
Environment
Europe
Exacycle
Expander
Faculty Institute
Faculty Summit
Flu Trends
Fusion Tables
gamification
Gboard
Gmail
Google Accelerated Science
Google Books
Google Brain
Google Cloud Platform
Google Docs
Google Drive
Google Genomics
Google Maps
Google Photos
Google Play Apps
Google Science Fair
Google Sheets
Google Translate
Google Trips
Google Voice Search
Google+
Government
grants
Graph
Graph Mining
Hardware
HCI
Health
High Dynamic Range Imaging
ICCV
ICLR
ICML
ICSE
Image Annotation
Image Classification
Image Processing
Inbox
India
Information Retrieval
internationalization
Internet of Things
Interspeech
IPython
Journalism
jsm
jsm2011
K-12
Kaggle
KDD
Keyboard Input
Klingon
Korean
Labs
Linear Optimization
localization
Low-Light Photography
Machine Hearing
Machine Intelligence
Machine Learning
Machine Perception
Machine Translation
Magenta
MapReduce
market algorithms
Market Research
materials science
Mixed Reality
ML
ML Fairness
MOOC
Moore's Law
Multimodal Learning
NAACL
Natural Language Processing
Natural Language Understanding
Network Management
Networks
Neural Networks
NeurIPS
Nexus
Ngram
NIPS
NLP
On-device Learning
open source
operating systems
Optical Character Recognition
optimization
osdi
osdi10
patents
Peer Review
ph.d. fellowship
PhD Fellowship
PhotoScan
Physics
PiLab
Pixel
Policy
Professional Development
Proposals
Public Data Explorer
publication
Publications
Quantum AI
Quantum Computing
Recommender Systems
Reinforcement Learning
renewable energy
Research
Research Awards
resource optimization
Responsible AI
Robotics
schema.org
Search
search ads
Security and Privacy
Self-Supervised Learning
Semantic Models
Semi-supervised Learning
SIGCOMM
SIGMOD
Site Reliability Engineering
Social Networks
Software
Sound Search
Speech
Speech Recognition
statistics
Structured Data
Style Transfer
Supervised Learning
Systems
TensorBoard
TensorFlow
TPU
Translate
trends
TTS
TV
UI
University Relations
UNIX
Unsupervised Learning
User Experience
video
Video Analysis
Virtual Reality
Vision Research
Visiting Faculty
Visualization
VLDB
Voice Search
Wiki
wikipedia
WWW
Year in Review
YouTube
Archive
2022
may
abr
mar
feb
ene
2021
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2020
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2019
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2018
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2017
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2016
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2015
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2014
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2013
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2012
dic
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2011
dic
nov
sep
ago
jul
jun
may
abr
mar
feb
ene
2010
dic
nov
oct
sep
ago
jul
jun
may
abr
mar
feb
ene
2009
dic
nov
ago
jul
jun
may
abr
mar
feb
ene
2008
dic
nov
oct
sep
jul
may
abr
mar
feb
2007
oct
sep
ago
jul
jun
feb
2006
dic
nov
sep
ago
jul
jun
abr
mar
feb
Feed
Follow @googleai
Give us feedback in our
Product Forums
.