Encoder-Decoder Manifold Alignment for Idempotent Generation

2026-06-20

AI Summary

This work addresses the challenge of achieving strict idempotence in generative models under repeated application, where output drift arises due to geometric inconsistencies between the data manifolds learned by the encoder and decoder. The study identifies this manifold misalignment as the key cause of idempotence failure—a previously unexamined issue—and introduces a novel training framework that explicitly aligns the geometric structures of both components. By enforcing the encoder’s projection and the decoder’s reconstruction to share a common underlying manifold during training, the proposed method substantially reduces idempotence error, yielding perfectly consistent outputs across repeated generations. Empirical results demonstrate significant improvements in identity preservation and information stability for image generation and editing tasks.

Abstract

Recently, several learning paradigms have been introduced to enforce idempotency in generative models. The goal is to ensure that repeated application of a model leaves samples unchanged once they lie on the target data manifold. In practice, however, many of these approaches fail to achieve exact fixed points, leading to instability and drift under repeated application. In this work, we argue that a key reason for this failure is a geometric mismatch between the manifolds learned by the encoder and decoder. The encoder projects inputs onto one latent manifold, while the decoder implicitly learns to reconstruct data from a different manifold. This discrepancy prevents the model from learning truly idempotent mappings. To address this issue, we propose a new training framework that explicitly closes this gap by forcing the encoder and decoder to learn consistent representations of the same underlying data manifold. By aligning the geometry of these components, our method encourages stable projections. Empirically, we show that our approach achieves significantly lower idempotency error and consistently regenerates identical outputs under repeated application, compared to existing methods. We demonstrate the effectiveness of the proposed framework on both image generation and image editing tasks. Finally, we show that enforcing idempotency in this manner improves identity preservation and information stability, leading to more realistic and controllable generative editing models.

Problem

• idempotency
• manifold alignment
• encoder-decoder mismatch
• generative models
• fixed points

Innovation

• idempotent generation
• manifold alignment
• encoder-decoder consistency
• generative modeling
• geometric representation learning