Tasks SotA Datasets Papers Methods Submit About

Papers With Code 2

A community resource for machine learning research: papers, code, benchmarks, and state-of-the-art results.

Explore

Notable Benchmarks All SotA Datasets Papers Methods

Community

Submit Results About

Data sourced from the PWC Archive (CC-BY-SA 4.0). Built by the community, for the community.

Models/InfoGAN

InfoGAN

Reported on 7 benchmarks across 2 tasks · 1 paper · 6 SOTA

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Medical6 results

Image GenerationonCUB 128 x 128
FID· 2016-06-12
13.2
best: 2.79 (Projected GAN)
SOTA
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657
Image GenerationonCUB 128 x 128
Inception score· 2016-06-12
47.32
best: 52.53 (FineGAN)
SOTA
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657
Image GenerationonStanford Cars
FID· 2016-06-12
17.63
best: 2.09 (Projected GANs)
SOTA
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657
Image GenerationonStanford Cars
Inception score· 2016-06-12
28.62
best: 32.62 (FineGAN)
SOTA
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657
Image GenerationonStanford Dogs
FID· 2016-06-12
29.34
best: 11.75 (Projected GAN)
SOTA
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657
Image GenerationonStanford Dogs
Inception score· 2016-06-12
43.16
best: 46.92 (FineGAN)
SOTA
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657

Computer Vision1 result

Image ClassificationonMNIST
Accuracy· 2016-06-12
95
best: 99.87 (Branching/Merging CNN + Homogeneous Vector Capsules)
InfoGAN: Interpretable Representation Learning by Information Maximizing Generative Adversarial Nets arXiv:1606.03657