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Research map / Label-efficient and robust learning / Recognition and long-tail

Learning to Sketch With Shortcut Cycle Consistency

CVPR 2018 · Label-efficient and robust learning › Recognition and long-tail · approach: Correspondence and causal representation learning · 112 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the full hybrid model with shortcut cycle consistency instead of Pix2seq one-way supervised translation (ablation)Photo-to-sketch synthesis · ShoeV2Recognition acc.@1: 51.50% -> 53.50%Helped
Use the full hybrid model with shortcut cycle consistency instead of Pix2seq one-way supervised translation (ablation)Photo-to-sketch synthesis · ShoeV2Retrieval acc.@1: 4.50% -> 6.00%Helped
Use the full hybrid model with shortcut cycle consistency instead of CycleGAN-S hybrid translationPhoto-to-sketch synthesis · ChairV2Retrieval acc.@1: 6.00% -> 8.00%Helped
Add synthetic photo-sketch pretraining for FG-SBIRFG-SBIR model · ShoeV2acc.@1: 30.33% -> 32.43%Helped
Add synthetic photo-sketch pretraining for FG-SBIRFG-SBIR model · ShoeV2acc.@10: 76.28% -> 77.48%Helped
Replace shortcut within-domain reconstruction with conventional full cycle consistency (ablation)Photo-to-sketch synthesis · QMUL-Shoe-Chair-V2photo-to-sketch synthesis qualityHurt
Add adversarial loss to the photo decoder (ablation)Photo-to-sketch synthesis model · QMUL-Shoe-Chair-V2photo-to-sketch synthesis qualityHurt

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