Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery

• URL: http://arxiv.org/abs/2606.17782v1
• Date: Tue, 16 Jun 2026 10:58:51 GMT
• Title: Blind Recovery of Latent Domains via Unsupervised Symmetry Discovery
• Abstract summary: We propose an unsupervised framework for recovering latent domains and signals by discovering symmetries of the data distribution.<n>Our framework models observations as linear measurements of signals sampled from a latent random field.<n>Experiments show that the method recovers latent domains and signals from unstructured observations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Primary motivation in blind inverse problems is to recover signals of interest from corrupted observations without knowing the obfuscating mechanism. Blind deconvolution is a prominent approach when the corruption is convolutional, but it is not applicable when general linear transformations obfuscate the domain structure. In this work, we propose an unsupervised framework for recovering latent domains and signals by discovering symmetries of the data distribution. Our framework models observations as linear measurements of signals sampled from a latent random field, and optimizes a shallow group-convolutional network by imposing stationarity and locality regularization at the model output. The model learns a latent symmetry action and an appropriate filter, thereby mapping unstructured observations to a symmetry-based representation that reveals latent signals. Experiments on stochastic processes, Ising models, shuffled and bit-scrambled images, and neural recordings show that the method recovers latent domains and signals from unstructured observations, suggesting symmetry discovery as a new direction for unsupervised structure learning and blind inverse problems.

Related papers

• Curved Inference II: Sleeper Agent Geometry - Extending Interpretability Beyond Probes [0.0]
  This paper extends Anthropic's Sleeper Agents research, which showed artificial backdoors persist through safety training & can be detected by linear probes with >99% accuracy.<n>We introduce a naturalistic methodology using multi-turn context windows that simulates realistic deceptive reasoning without artificial triggers or supervised backdoor insertion.<n> Geometric structure reliably predicts semantic classification, with statistically significant differences in surface area across five prompt strategies & two model families.
  arXiv  Detail & Related papers  (2026-08-25T03:51:20Z)
• Need We Teach Foundation Models What is a Generative Image? Gradient-Free Generative Artifact Detection via Analytic Spectral Adaptation [3.881211374324378]
  Adapting foundation models to detect generative artifacts via gradient-based updates compromises their intrinsic representations.<n>We propose a gradient-free methodology reframing detection from binary classification to an out-of-distribution (OOD) anomaly measurement problem.
  arXiv  Detail & Related papers  (2026-06-03T13:04:23Z)
• Noise & pattern: identity-anchored Tikhonov regularization for robust structural anomaly detection [58.535473924035365]
  Anomaly detection plays a pivotal role in automated industrial inspection, aiming to identify subtle or rare defects in otherwise uniform visual patterns.<n>We tackle structural anomaly detection using a self-supervised autoencoder that learns to repair corrupted inputs.<n>We introduce a corruption model that injects artificial disruptions into training images to mimic structural defects.
  arXiv  Detail & Related papers  (2025-11-10T15:48:50Z)
• Generative Model Inversion Through the Lens of the Manifold Hypothesis [98.37040155914595]
  Model inversion attacks (MIAs) aim to reconstruct class-representative samples from trained models.<n>Recent generative MIAs utilize generative adversarial networks to learn image priors that guide the inversion process.
  arXiv  Detail & Related papers  (2025-09-24T14:39:25Z)
• Physics-Guided Dual Implicit Neural Representations for Source Separation [70.38762322922211]
  We develop a self-supervised machine-learning approach for source separation using a dual implicit neural representation framework.<n>Our method learns directly from the raw data by minimizing a reconstruction-based loss function.<n>Our method offers a versatile framework for addressing source separation problems across diverse domains.
  arXiv  Detail & Related papers  (2025-07-07T17:56:31Z)
• Generalized Linear Mode Connectivity for Transformers [87.32299363530996]
  A striking phenomenon is linear mode connectivity (LMC), where independently trained models can be connected by low- or zero-loss paths.<n>Prior work has predominantly focused on neuron re-ordering through permutations, but such approaches are limited in scope.<n>We introduce a unified framework that captures four symmetry classes: permutations, semi-permutations, transformations, and general invertible maps.<n>This generalization enables, for the first time, the discovery of low- and zero-barrier linear paths between independently trained Vision Transformers and GPT-2 models.
  arXiv  Detail & Related papers  (2025-06-28T01:46:36Z)
• Learning Linear Causal Representations from Interventions under General Nonlinear Mixing [52.66151568785088]
  We prove strong identifiability results given unknown single-node interventions without access to the intervention targets. This is the first instance of causal identifiability from non-paired interventions for deep neural network embeddings.
  arXiv  Detail & Related papers  (2023-06-04T02:32:12Z)
• Shuffled Multi-Channel Sparse Signal Recovery [16.333381000882486]
  We pose it as a signal reconstruction problem where we have lost correspondences between the samples and their respective channels. We show that the problem is equivalent to a structured unlabeled sensing problem, and establish sufficient conditions for unique recovery. We propose a robust reconstruction method that combines sparse signal recovery with robust linear regression for the two-channel case.
  arXiv  Detail & Related papers  (2022-12-14T17:46:17Z)
• Self-Supervised Training with Autoencoders for Visual Anomaly Detection [61.62861063776813]
  We focus on a specific use case in anomaly detection where the distribution of normal samples is supported by a lower-dimensional manifold. We adapt a self-supervised learning regime that exploits discriminative information during training but focuses on the submanifold of normal examples. We achieve a new state-of-the-art result on the MVTec AD dataset -- a challenging benchmark for visual anomaly detection in the manufacturing domain.
  arXiv  Detail & Related papers  (2022-06-23T14:16:30Z)
• Learning to Discover Reflection Symmetry via Polar Matching Convolution [33.77926792753373]
  We introduce a new convolutional technique, dubbed the polar matching convolution, which leverages a polar feature pooling, a self-similarity encoding, and a kernel design for axes of different angles. The proposed high-dimensional kernel convolution network effectively learns to discover symmetry patterns from real-world images. Experiments demonstrate that our method outperforms state-of-the-art methods in terms of accuracy and robustness.
  arXiv  Detail & Related papers  (2021-08-30T01:50:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.