Resolution Invariant Autoencoder

doi:10.1007/978-3-032-05472-2_16

2026-01-01

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DOI：10.1007/978-3-032-05472-2_16

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深度学习在医学图像分析方面取得了显著进展，但图像分辨率的差异仍然是一个被忽视的挑战。大多数方法通过重采样图像来应对这一问题，导致信息损失或计算效率低下。虽然针对特定任务已有解决方案，但尚未提出统一的方法。我们提出了一种分辨率不变的自动编码器，通过在网络的每一层学习可变调整大小的过程来适应空间调整大小，取代了传统固定因子的2倍空间下/上采样。这确保了潜在空间的分辨率保持一致，无论输入或输出分辨率如何。我们的模型能够在图像潜在表示上执行各种下游任务，同时在不同分辨率下保持性能，克服了传统方法的不足。我们通过不确定性感知的超分辨率、分类和生成建模任务证明了其有效性，并展示了我们的方法在不同分辨率下以最小的性能损失优于传统基准。

Keyword:

Resolution Invariance

Autoencoder

Super-resolution

Classification

Generative Modelling

Latent Diffusion Model

期刊

DEEP GENERATIVE MODELS, DGM4MICCAI 2025

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论文数:

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机构

university of london

学者数:

21.5W

论文数: 19.7W

被引数: 305

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