Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets
Small-scale image classification is often limited by the scarcity of training data. Generative data augmentation (GDA) based on pretrained generative models has emerged as an effective solution. However, existing methods rely on task-agnostic augmentation strategies that overlook downstream model needs. Although recent dynamic GDA methods incorporate model feedback to guide augmentation, they still struggle to reliably determine sample-specific augmentation strengths and adapt augmentation strategies to different image regions while balancing image diversity and class semantics. To address t
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- FuzzySimilar title/name (fuzzy) · 84%huggingface/datasets →
“Fuzzy title match (0.92): “Learning-State-Aware Dynamic Generative Data Augmentation on” ≈ “huggingface/datasets””
- FuzzySimilar title/name (fuzzy) · 84%GoogleCloudPlatform/generative-ai →
“Fuzzy title match (0.92): “Learning-State-Aware Dynamic Generative Data Augmentation on” ≈ “GoogleCloudPlatform/generative-ai””
- FuzzyOverlapping authors or contributors · 62%affaan-m/ECC →
“Shared author/contributor keys: jiang”
- FuzzyOverlapping authors or contributors · 62%BerriAI/litellm →
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- FuzzySimilar title/name (fuzzy) · 59%steven2358/awesome-generative-ai →
“Fuzzy title match (0.73): “Learning-State-Aware Dynamic Generative Data Augmentation on” ≈ “steven2358/awesome-generative-ai””
- LinkedLinked via arxiv author · 85%Ting Xiang →
“Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets”
- LinkedLinked via arxiv author · 85%Chenxi Deng →
“Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets”
- LinkedLinked via arxiv author · 85%Jinhui Zhao →
“Learning-State-Aware Dynamic Generative Data Augmentation on Small-Scale Datasets”