用扩散模型处理天文不规则多模态序列，提升表征效果
Diffusion Autoencoders with Perceivers for Long, Irregular and Multimodal Astronomical Sequences
- 用Perceiver编码器压缩异构天文数据，扩散解码器重建
- 在多个天文数据集上重建误差更低，潜空间更判别
- 适合处理长时序、非均匀、多源的科学数据
自监督学习已成为表示学习的核心策略，但现有架构大多仅在图像、音频和视频等规则采样数据上验证。在许多科学领域，数据以长序列、不规则采样和多模态形式存在。为从这类数据中提取语义信息，我们提出扩散自编码器与感知器结合的框架（daep）。daep将异构观测数据分词，通过Perceiver编码器压缩，并利用Perceiver-IO扩散解码器重建，实现多样化数据场景下的可扩展学习。为评估该架构，我们基于Perceiver设计改进了掩码自编码器（maep）作为基线。在多个光谱和测光天文数据集上，daep的重建误差更低，潜空间更具判别性，且更优地保留细粒度结构，显著优于VAE和maep基线。结果表明，daep是处理不规则、异构序列数据的有效框架。
原文摘要 · Abstract (English)
Self-supervised learning has become a central strategy for representation learning, but the majority of architectures used for encoding data have only been validated on regularly-sampled inputs such as images, audios. and videos. In many scientific domains, data instead arrive as long, irregular, and multimodal sequences. To extract semantic information from these data, we introduce the Diffusion Autoencoder with Perceivers (daep). daep tokenizes heterogeneous measurements, compresses them with a Perceiver encoder, and reconstructs them with a Perceiver-IO diffusion decoder, enabling scalable learning in diverse data settings. To benchmark the daep architecture, we adapt the masked autoencoder to a Perceiver encoder/decoder design, and establish a strong baseline (maep) in the same architectural family as daep. Across diverse spectroscopic and photometric astronomical datasets, daep achieves lower reconstruction errors, produces more discriminative latent spaces, and better preserves fine-scale structure than both VAE and maep baselines. These results establish daep as an effective framework for scientific domains where data arrives as irregular, heterogeneous sequences.
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