Pith. sign in

REVIEW 6 cited by

Contrastive Learning for Unpaired Image-to-Image Translation

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 2007.15651 v3 pith:VH7GF47E submitted 2020-07-30 cs.CV cs.LG

Taesung Park , Alexei A. Efros , Richard Zhang , Jun-Yan Zhu This is my paper
classification cs.CVcs.LG
keywords translationcontrastiveimageimage-to-imagelearningmethodsettingcorresponding
verification ladder T0 review T1 audit T2 compute T3 formal
0 comments
read the original abstract

In image-to-image translation, each patch in the output should reflect the content of the corresponding patch in the input, independent of domain. We propose a straightforward method for doing so -- maximizing mutual information between the two, using a framework based on contrastive learning. The method encourages two elements (corresponding patches) to map to a similar point in a learned feature space, relative to other elements (other patches) in the dataset, referred to as negatives. We explore several critical design choices for making contrastive learning effective in the image synthesis setting. Notably, we use a multilayer, patch-based approach, rather than operate on entire images. Furthermore, we draw negatives from within the input image itself, rather than from the rest of the dataset. We demonstrate that our framework enables one-sided translation in the unpaired image-to-image translation setting, while improving quality and reducing training time. In addition, our method can even be extended to the training setting where each "domain" is only a single image.

Discussion (0). Sign in to comment.

Forward citations

Cited by 6 Pith papers

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

  1. Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

    cs.CV 2026-07 conditional novelty 6.0 of 10

    Hallo4D uses vision-language models to detect and correct spatial and temporal mistakes in AI-generated 3D and 4D content, improving consistency without retraining the base generators.

  2. Hallo4D: Multi-Modal Hallucination Mitigation for Consistent Spatio-Temporal Generation

    cs.CV 2026-07 conditional novelty 5.0 of 10

    Hallo4D mitigates 3D/4D generation hallucinations via LMM-based detection, multi-model voting correction, and motion-aware optimization without retraining base generators.

  3. Style-Based Neural Architectures for Real-Time Weather Classification

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    Three style-based neural architectures are proposed for real-time weather classification from images, with two truncated ResNet variants claimed to outperform prior methods and generalize across public datasets.

  4. Stylistic-STORM (ST-STORM) : Perceiving the Semantic Nature of Appearance

    cs.CV 2026-04 unverdicted novelty 5.0 of 10

    ST-STORM introduces a dual-branch SSL framework that disentangles semantic content from stylistic appearance using gated latent streams, JEPA for content invariance, and adversarial constraints for style capture.

  5. Heuristic Style Transfer for Real-Time, Efficient Weather Attribute Detection

    cs.CV 2026-04 conditional novelty 5.0 of 10

    Lightweight multi-task models using Gram matrices and PatchGAN-style architectures detect 53 weather classes from RGB images with F1 scores above 96% internally and 78% zero-shot externally, supported by a new 503k-im...

  6. HyPER-GAN: Hybrid Patch-Based Image-to-Image Translation for Real-Time Photorealism Enhancement in Game Engines

    cs.CV 2026-03 conditional novelty 5.0 of 10

    A lightweight hybrid-patch GAN enhances synthetic game images toward photorealism in real time, beating prior lightweight paired translators on speed, KID, and semantic consistency.

Pith tools