AI 中文总结
本文提出自重建动力学（SRD）引导的重建精化方法，利用冻结自编码器的重复重建轨迹预测潜在修正，无需测试时优化，在六个数据集上恢复约40%的MSE差距并提升PSNR，证实了样本特异性精化信号的有效性。
AI 中文摘要
标准自编码器（AE）推理使用单次编码器-解码器传递，但在固定解码器下，潜在表示可能并非对每个样本都是最优的。我们提出疑问：训练好的AE能否揭示用于改进其自身重建的信息？将冻结的AE重复应用于其重建结果，会产生瞬时的图像空间和潜在空间轨迹，称为自重建动力学（SRD）。尽管在本文研究的AE中这会降低保真度，但SRD包含用于纠正重建的样本特定信息。我们提出SRD引导的重建精化（SRD-RR），该方法从短SRD预测潜在修正，AE保持冻结，无需逐样本测试时优化。我们还引入了MSE-recov，即相对于经验解码器优化参考的MSE恢复比率。在六个数据集上，SRD-RR在一次转换中恢复了经验可恢复MSE差距的38.6%，两次转换恢复45.3%。一种不直接访问原始图像、使用SRD派生的伪目标训练的两转换变体，实现了40.7%的恢复率和1.74 dB的平均PSNR增益。移除轨迹信息会降低增益，而跨样本轨迹分配导致严重退化，证实了强样本特异性。非线性SRD条件精化始终优于固定和训练的线性潜在修正。在基于预训练DINOv2的表示自编码器（RAE）上，其潜在动力学显著不同，SRD条件再次改进了匹配的无轨迹预测器。然而，像素MSE潜在精化揭示了像素保真度与感知质量之间的强烈不匹配，而SRD派生的伪目标缓解了这种退化。总体而言，SRD是一种有用的样本特定精化信号，而目标函数决定了它如何转化为像素和感知质量。
英文摘要
Standard autoencoder (AE) inference uses a single encoder-decoder pass, though the latent may not be optimal for each sample under a fixed decoder. We ask whether a trained AE can reveal information for improving its own reconstruction. Repeated application of a frozen AE to its reconstruction produces transient image- and latent-space trajectories, termed Self-Reconstruction Dynamics (SRD). Although this degrades fidelity in the AEs studied here, SRD contains sample-specific information for correcting the reconstruction. We propose SRD-guided Reconstruction Refinement (SRD-RR), which predicts a latent correction from a short SRD with the AE frozen and no per-sample test-time optimization. We also introduce MSE-recov, an MSE recovery ratio relative to an empirical decoder-optimized reference. Across six datasets, SRD-RR recovers 38.6% of the empirically recoverable MSE gap with one transition and 45.3% with two. A two-transition variant trained without direct access to original images, using an SRD-derived pseudo-target, achieves 40.7% recovery and a 1.74 dB average PSNR gain. Removing trajectory information reduces the gain, while cross-sample trajectory assignment causes severe degradation, confirming strong sample specificity. Nonlinear SRD-conditioned refinement consistently outperforms fixed and trained linear latent correction. On a pretrained DINOv2-based representation autoencoder (RAE) with substantially different latent dynamics, SRD conditioning again improves a matched trajectory-free predictor. However, pixel-MSE latent refinement reveals a strong mismatch between pixel fidelity and perceptual quality, while the SRD-derived pseudo-target mitigates this degradation. Overall, SRD is a useful sample-specific refinement signal, while the objective determines how it translates into pixel and perceptual quality.
Comments23 pages, 12 figures
