Autoencoder self-reconstruction dynamics improve image refinement accuracy
Self-Reconstruction Dynamics for Autoencoder Reconstruction Refinement
Machine Learning
Summary
Autoencoders try to recreate images through a process of compressing and decompressing them once. The authors found that repeatedly feeding a reconstructed image back into the same autoencoder creates unique patterns called self-reconstruction dynamics, which can help improve the image even further. They developed a method to use these patterns to adjust the compressed data and make better refinements without needing extra tuning for each image. Their approach works across different datasets and improves image quality measurably.
What this means in practice
- •For computer vision engineers: Enhance image reconstruction quality in autoencoder-based systems by refining latent representations using self-reconstruction dynamics without extra test-time optimization.
- •For machine learning developers: Improve the fidelity of latent space representations with sample-specific corrections derived from repeated autoencoder passes to boost downstream generative or analytic tasks.
Authors
Hitoshi Iyatomi
Abstract
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.