Pith. sign in

REVIEW 1 cited by

Improved generator objectives for GANs

Not yet reviewed by Pith; the record is open.

Re-run · record.json Download PDF Read on arXiv ↗

This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.

SPECIMEN: schema-true, not a live event

T0 review · schema-true

One-sentence machine reading of the paper's core claim.

pith:XXXXXXXX · record.json · timestamp

arxiv 1612.02780 v1 pith:6ZSJCAQF submitted 2016-12-08 cs.LG stat.ML

Ben Poole , Alexander A. Alemi , Jascha Sohl-Dickstein , Anelia Angelova This is my paper · ORCID
classification cs.LGstat.ML
keywords generatorobjectivessamplediversityimprovedtargetalternatingapproximate
verification ladder T0 review T1 audit T2 compute T3 formal

Signed reviews

No signed human review yet.

0 comments
abstract

We present a framework to understand GAN training as alternating density ratio estimation and approximate divergence minimization. This provides an interpretation for the mismatched GAN generator and discriminator objectives often used in practice, and explains the problem of poor sample diversity. We also derive a family of generator objectives that target arbitrary $f$-divergences without minimizing a lower bound, and use them to train generative image models that target either improved sample quality or greater sample diversity.

Discussion (0). Continue with ORCID to comment.

Forward citations

Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. DGSAN: Discrete Generative Self-Adversarial Network

    cs.LG 2019-08 conditional novelty 6.0 of 10

    DGSAN trains an explicit discrete generator by iteratively maximizing an objective that compares the new generator's density to the old one's, avoiding gradient passing through discrete samples.

Pith tools