压缩潜在流形：通过变分自编码器进行分辨率外推
Distilling Latent Manifolds: Resolution Extrapolation by Variational Autoencoders
AI总结：
本文发现变分自编码器压缩器在低分辨率下表现差，但能有效外推到更高分辨率，通过分析发现高分辨率输入更接近教师模型的流形，实验表明简单的分辨率映射可提升多种图像质量指标。
摘要：
变分自编码器（VAE）编码器在现代生成模型中起关键作用，但其计算成本常促使使用知识蒸馏或量化以获得紧凑替代方案。现有研究通常认为模型在接近训练数据分布的样本上表现更好，但本文报告了VAE编码器蒸馏中的反直觉现象：仅在低分辨率上蒸馏的紧凑编码器在原生分辨率下重建性能差，但评估在更高、未见过的输入分辨率时表现显著提升。尽管从未在超过256²的分辨率上训练，蒸馏编码器仍能有效泛化到512²分辨率输入，部分继承教师模型的分辨率偏好。我们进一步分析不同分辨率下的潜在分布，发现高分辨率输入产生的潜在表示更接近教师模型的流形。通过在ImageNet-256上的广泛实验，我们发现简单的分辨率映射——在编码前上采样输入，在评估时下采样重建结果——在PSNR、MSE、SSIM、LPIPS和rFID等指标上带来显著提升。这些发现表明，VAE编码器蒸馏学习的是分辨率一致的潜在流形，而非分辨率特定的像素映射。这也意味着在内存、时间和高分辨率数据集上的高训练成本并非蒸馏具有高分辨率图像重建能力的VAE的必要条件。在低分辨率数据集上，蒸馏模型仍能学习教师模型在高分辨率图像重建中的详细知识。
英文摘要：
Variational Autoencoder (VAE) encoders play a critical role in modern generative models, yet their computational cost often motivates the use of knowledge distillation or quantification to obtain compact alternatives. Existing studies typically believe that the model work better on the samples closed to their training data distribution than unseen data distribution. In this work, we report a counter-intuitive phenomenon in VAE encoder distillation: a compact encoder distilled only at low resolutions exhibits poor reconstruction performance at its native resolution, but achieves dramatically improved results when evaluated at higher, unseen input resolutions. Despite never being trained beyond $256^2$ resolution, the distilled encoder generalizes effectively to $512^2$ resolution inputs, partially inheriting the teacher model's resolution preference.We further analyze latent distributions across resolutions and find that higher-resolution inputs produce latent representations more closely aligned with the teacher's manifold. Through extensive experiments on ImageNet-256, we show that simple resolution remapping-upsampling inputs before encoding and downsampling reconstructions for evaluation-leads to substantial gains across PSNR, MSE, SSIM, LPIPS, and rFID metrics. These findings suggest that VAE encoder distillation learns resolution-consistent latent manifolds rather than resolution-specific pixel mappings. This also means that the high training cost on memory, time and high-resolution datasets are not necessary conditions for distilling a VAE with high-resolution image reconstruction capabilities. On low resolution datasets, the distillation model still could learn the detailed knowledge of the teacher model in high-resolution image reconstruction.
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