The Spatially-Correlative Loss for Various Image Translation Tasks
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Abstract (arXiv)
We propose a novel spatially-correlative loss that is simple, efficient and yet effective for preserving scene structure consistency while supporting large appearance changes during unpaired image-to-image (I2I) translation. Previous methods attempt this by using pixel-level cycle-consistency or feature-level matching losses, but the domain-specific nature of these losses hinder translation across large domain gaps. To address this, we exploit the spatial patterns of self-similarity as a means of defining scene structure. Our spatially-correlative loss is geared towards only capturing spatial relationships within an image rather than domain appearance. We also introduce a new self-supervised learning method to explicitly learn spatially-correlative maps for each specific translation task. We show distinct improvement over baseline models in all three modes of unpaired I2I translation: single-modal, multi-modal, and even single-image translation. This new loss can easily be integrated into existing network architectures and thus allows wide applicability.
Key facts. Cited by 66 papers in the map (data through 14 Oct 2026). Builds on 48 of its 59 references that are in the map. First posted on arXiv 2 Apr 2021.
Builds on
48 of 59 references.
References · 48
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Citing papers per year: 2021: 6; 2022: 18; 2023: 23; 2024: 9; 2025: 7; 2026: 3.
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