A dynamic pruning approach for cross-domain few-shot image generation

doi:10.11996/JG.j.2095-302X.2026010131

Abstract

Abstract:

Few-shot image generation has important application value in fields such as medical imaging and artistic creation. In recent years, significant research progress has been made in this task, with mainstream approaches typically relying on transferring generative models pretrained on large-scale source domain datasets to target domains to mitigate data-scarcity challenges. However, when substantial semantic gaps exist between source and target domains, direct transfer often introduced incompatible source-specific features, degrading image realism and style consistency. Although existing methods have removed redundant features via static pruning strategies, such as fixed-threshold filter pruning, they struggle to adapt to the dynamic evolution of features across different layers of deep networks, often resulting in the mistaken removal of general low-level features while retaining redundant high-level ones, thereby affecting the adaptation performance and generation quality of the model. To address this, a dynamic pruning method based on filter-importance estimation was proposed. Specifically, the method continuously tracked the changes in Fisher information of each layer’s filters during training to evaluate their importance for image generation quality. Based on the Fisher information, a cumulative importance weight-based adaptive pruning mechanism was constructed to dynamically determine the pruning ratio for each layer, enabling more precise removal of redundant or incompatible filters while preserving general structural semantic information. Experiments were conducted on several representative few-shot target domains, and results showed that the proposed method significantly outperformed existing approaches in terms of image quality (Frechet Inception Distance, FID) and image diversity (Intra-domain Learned Perceptual Image Patch Similarity, Intra-LPIPS). In target domains exhibiting significant semantic differences from the source domain, the proposed method achieved superior FID scores compared with the current state-of-the-art methods, demonstrating its stability and superiority for cross-domain few-shot image generation tasks.

Key words: few-shot image generation, transfer learning, dynamic pruning, Fisher information, generative adversarial networks

CLC Number:

TP391.4
TP18

LI Shiliang, FANG Qiang, WANG Yihua, SHI Yifei, WANG Zhuo, LI Zeyu, XIE Yunfei, WANG Jia. A dynamic pruning approach for cross-domain few-shot image generation[J]. Journal of Graphics, 2026, 47(1): 131-142.

Figures/Tables 11

Fig. 1 Fisher information variation across different-depth convolutional layers in RICK

Fig. 2 Depth-wise analysis of pruned filters in generator convolutional layers using RICK

Fig. 3 Framework of few-shot image generation based on importance-weighted dynamic pruning

Fig. 4 Illustration of source and target domains((a) Source domain；(b) Target domain)

Fig. 5 Performance comparison of various FSIG approaches on the Babies dataset

Fig. 6 Performance comparison of various FSIG approaches on the AFHQ-Cat dataset

Table 1 Comparison of FID (↓) scores of different FSIG methods on various target domains

方法	Backbone	Babies	Sunglasses	Sketches	AFHQ-Cat
TGAN^[25]	StyleGAN	101.58	55.97	53.41	64.68
TGAN+ADA^[12]	StyleGAN	97.91	53.64	66.99	80.16
FreezeD^[26]	StyleGAN	96.25	46.95	46.54	63.60
CDC^[23]	StyleGAN2	69.13	41.45	45.67	176.21
DCL^[29]	StyleGAN2	56.48	37.66	57.72	156.82
EWC^[27]	StyleGAN2	79.93	49.41	71.25	74.61
DDPM-PA^[33]	DDPM	48.92	34.75	一	一
AdAM^[30]	StyleGAN2	48.83	28.03	38.11	58.07
RICK^[22]	StyleGAN2	39.39	25.22	40.52	53.27
CRDI^[34]	DDPM	48.52	24.62	一	一
DAP(Ours)	StyleGAN2	36.97	24.13	37.96	44.40

Fig. 7 The comparison of style transfer ability between RICK and DAP on the Sketches dataset ((a) Target domain; (b) Source domain; (c) RICK; (d) Our method)

Fig. 8 Layer-wise Comparison of pruned filters between DAP and RICK in generator convolutional layers ((a) Shallow layer; (b) Middle layer; (c) Deep layer)

Table 2 Ablation study of DAP integration into AdAM: FID comparison on multiple target domains

方法	Backbone	Babies	Sunglasses	Sketches	AFHQ-Cat
AdAM^[30]	StyleGAN2	48.83	28.03	38.11	58.07
AdAM+DAP(Ours)	StyleGAN2	46.14(-2.69)	25.57(-2.46)	38.06(-0.05)	50.12(-7.95)

Fig. 9 Impact of hyperparameter α on FID scores across different target domains

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