通过探索生成模型的潜在空间，精准填补图像数据缺失值。
FILLER: Feature Imputation via Latent Location Exploration and Retrieval
- 在生成模型的二维潜空间中搜索，定位并填充缺失特征。
- 在多种缺失模式下，RMSE、PSNR、SSIM均优于现有方法。
- 适合图像修复与下游任务，尤其适用于结构化缺失场景。
现实世界机器学习应用中，不完整观测构成根本挑战。尽管已有多种解决方案，现有模型仍难以兼顾可扩展性与结构一致性。本文提出FILLER，一种基于生成模型潜空间探索的特征填补方法。该方法利用训练于完整数据的生成模型（本研究中为G-NeuroDAVIS）从潜空间生成样本，并在测试样本存在缺失时，通过迭代搜索确定合适填充值。研究还提供了迭代搜索收敛性的数学证明。FILLER在多个图像数据集上评估，覆盖随机与结构化缺失模式及不同复杂度。对比SOTA方法，其在RMSE、PSNR、SSIM上表现更优，且经Wilcoxon符号秩检验验证统计显著性。下游分类与聚类任务进一步证实了填补质量。
原文摘要 · Abstract (English)
In real-world machine learning applications, incomplete observations create a fundamental challenge. Researchers have come up with several ideas to address this crucial problem. However, current models still face challenges in balancing scalability and structural consistency. This study proposes a feature imputation method, called FILLER, that deliberately searches the two-dimensional latent space produced by a generative model and fills the missing values with appropriate entries. The generative model is trained on fully observed data to generate samples from the latent space, and FILLER uses this trained model to impute the values missing in the corrupted test samples. In this study, G-NeuroDAVIS serves the purpose of the generative model. This work also presents a mathematical proof on the convergence of the iterative search. Finally, FILLER has been evaluated on several image datasets under random and structured missingness patterns with varying levels of imputation complexities. In order to justify the efficacy of FILLER, it has been compared against existing state-of-the-art solution strategies in terms of RMSE, PSNR, and SSIM. In addition, Wilcoxon signed-rank test has been carried out to validate statistical significance. Moreover, downstream analyses (classification and clustering) have also established the quality of imputation in terms of standard metrics.
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