Semantics-Aware Generative Latent Data Augmentation for Learning in Low-Resource Domains

• URL: http://arxiv.org/abs/2602.02841v2
• Date: Mon, 09 Feb 2026 18:46:04 GMT
• Title: Semantics-Aware Generative Latent Data Augmentation for Learning in Low-Resource Domains
• Authors: Jaesung Bae, Minje Kim,
• Abstract summary: We propose GeLDA, a semantics-aware generative latent data augmentation framework.<n>Because this space is low-dimensional and concentrates task-relevant information compared to the input space, GeLDA enables efficient, high-quality data generation.<n>We validate GeLDA in two large-scale recognition tasks: (a) in zero-shot language-specific speech emotion recognition, GeLDA improves the Whisper-large baseline's unweighted average recall by 6.13%; and (b) in long-tailed image classification, it achieves 74.7% tail-class accuracy on ImageNet-LT.
• Score: 27.911250327145115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite strong performance in data-rich regimes, deep learning often underperforms in the data-scarce settings common in practice. While foundation models (FMs) trained on massive datasets demonstrate strong generalization by extracting general-purpose features, they can still suffer from scarce labeled data during downstream fine-tuning. To address this, we propose GeLDA, a semantics-aware generative latent data augmentation framework that leverages conditional diffusion models to synthesize samples in an FM-induced latent space. Because this space is low-dimensional and concentrates task-relevant information compared to the input space, GeLDA enables efficient, high-quality data generation. GeLDA conditions generation on auxiliary feature vectors that capture semantic relationships among classes or subdomains, facilitating data augmentation in low-resource domains. We validate GeLDA in two large-scale recognition tasks: (a) in zero-shot language-specific speech emotion recognition, GeLDA improves the Whisper-large baseline's unweighted average recall by 6.13%; and (b) in long-tailed image classification, it achieves 74.7% tail-class accuracy on ImageNet-LT, setting a new state-of-the-art result.

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