基于判别原型引导扩散的逼真遥感数据集蒸馏研究
Toward Realistic Remote Sensing Dataset Distillation with Discriminative Prototype-guided Diffusion
AI总结：
针对遥感图像解释依赖大量训练数据带来的高成本问题，提出判别原型引导扩散框架，通过提取原型、构建语义锚及筛选候选样本，将大规模数据集浓缩成有代表性的蒸馏数据集用于下游模型训练。
摘要：
近年来，受大规模基准数据集的推动，深度学习在遥感图像解释方面取得了显著成功。然而，对大量训练数据的依赖也带来了巨大的存储和计算成本。为应对这一挑战，本研究首次将数据集蒸馏的概念引入遥感图像解释领域。具体而言，我们提出了判别原型引导扩散（DPD），这是一种基于扩散的生成式蒸馏框架，它将大规模遥感数据集浓缩成一个紧凑且具有代表性的蒸馏数据集。为了提高合成样本的语义保真度和多样性，我们在潜在空间中为每个类别提取代表性原型。然后，我们在原型周围构建超球面语义锚，以引导反向去噪轨迹。此外，为了提高生成样本的判别质量，为每个原型生成多个候选样本，并使用对数边际准则由潜在分类器进行排序，选择最具判别力的候选样本形成最终的蒸馏数据集。在三个高分辨率遥感场景分类基准上的实验表明，所提出的方法可以为下游模型训练蒸馏出逼真、多样且具有判别力的样本。代码和预训练模型可在线获取（此https URL）。
英文摘要：
Recent years have witnessed the remarkable success of deep learning in remote sensing image interpretation, driven by the availability of large-scale benchmark datasets. However, this reliance on massive training data also brings substantial storage and computational costs. To address this challenge, this study introduces the concept of dataset distillation into the field of remote sensing image interpretation for the first time. Specifically, we propose discriminative prototype-guided diffusion (DPD), a diffusion-based generative distillation framework that condenses a large-scale remote sensing dataset into a compact and representative distilled dataset. To improve the semantic fidelity and diversity of the synthesized samples, we extract representative prototypes for each category in the latent space. We then construct hyperspherical semantic anchors around the prototypes to guide the reverse denoising trajectory. Furthermore, to enhance the discriminative quality of the generated samples, multiple candidates are generated for each prototype and ranked by a latent classifier using a logit-margin criterion, with the most discriminative candidates selected to form the final distilled dataset. Experiments on three high-resolution remote sensing scene classification benchmarks show that the proposed method can distill realistic, diverse, and discriminative samples for downstream model training. Code and pre-trained models are available online (https://github.com/YonghaoXu/DPD).
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
