Rethinking cross-domain semantic relation for few-shot image generation
Training well-performing Generative Adversarial Networks (GANs) with limited data has always been challenging. Existing methods either require sufficient data (over 100 training images) for training or generate images of low quality and low diversity. To solve this problem, we propose a new Cross-do...
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| Vydané v: | Applied intelligence (Dordrecht, Netherlands) Ročník 53; číslo 19; s. 22391 - 22404 |
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| Hlavní autori: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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New York
Springer US
01.10.2023
Springer Nature B.V |
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| ISSN: | 0924-669X, 1573-7497 |
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| Abstract | Training well-performing Generative Adversarial Networks (GANs) with limited data has always been challenging. Existing methods either require sufficient data (over 100 training images) for training or generate images of low quality and low diversity. To solve this problem, we propose a new Cross-domain Semantic Relation (CSR) loss. The CSR loss improves the performance of the generative model by maintaining the relationship between instances in the source domain and generated images. At the same time, a perceptual similarity loss and a discriminative contrastive loss are designed to further enrich the diversity of generated images and stabilize the training process of models. Experiments on nine publicly available few-shot datasets and comparisons with the current nine methods show that our approach is superior to all baseline methods. Finally, we perform ablation studies on the proposed three loss functions and prove that these three loss functions are essential for few-shot image generation tasks. Code is available at
https://github.com/gouayao/CSR
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| AbstractList | Training well-performing Generative Adversarial Networks (GANs) with limited data has always been challenging. Existing methods either require sufficient data (over 100 training images) for training or generate images of low quality and low diversity. To solve this problem, we propose a new Cross-domain Semantic Relation (CSR) loss. The CSR loss improves the performance of the generative model by maintaining the relationship between instances in the source domain and generated images. At the same time, a perceptual similarity loss and a discriminative contrastive loss are designed to further enrich the diversity of generated images and stabilize the training process of models. Experiments on nine publicly available few-shot datasets and comparisons with the current nine methods show that our approach is superior to all baseline methods. Finally, we perform ablation studies on the proposed three loss functions and prove that these three loss functions are essential for few-shot image generation tasks. Code is available at
https://github.com/gouayao/CSR
. Training well-performing Generative Adversarial Networks (GANs) with limited data has always been challenging. Existing methods either require sufficient data (over 100 training images) for training or generate images of low quality and low diversity. To solve this problem, we propose a new Cross-domain Semantic Relation (CSR) loss. The CSR loss improves the performance of the generative model by maintaining the relationship between instances in the source domain and generated images. At the same time, a perceptual similarity loss and a discriminative contrastive loss are designed to further enrich the diversity of generated images and stabilize the training process of models. Experiments on nine publicly available few-shot datasets and comparisons with the current nine methods show that our approach is superior to all baseline methods. Finally, we perform ablation studies on the proposed three loss functions and prove that these three loss functions are essential for few-shot image generation tasks. Code is available at https://github.com/gouayao/CSR. |
| Author | He, Yujie Li, Min Zhang, Yusen Gou, Yao Xing, Yuhang Lv, Yilong |
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| CitedBy_id | crossref_primary_10_1007_s11042_024_20517_z crossref_primary_10_1016_j_neunet_2025_107862 crossref_primary_10_1007_s11263_025_02357_y crossref_primary_10_1007_s10489_025_06379_4 |
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| Keywords | Cross-domain semantic relation Generative adversarial networks Few-shot image generation Perceptual similarity Contrastive learning |
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| Title | Rethinking cross-domain semantic relation for few-shot image generation |
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