Frozen Encoder

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18 papers in the last four weeks, up 350% on the four weeks before. 0.2% of all new papers.

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FILTR: Extracting Topological Features from Pretrained 3D Models
Apr 24, 2026Louis Martinez, Maks OvsjanikovPoint Clouds Topology
LIX, ´Ecole Polytechnique, IP Paris
Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning
Apr 17, 2026Gehan Zheng, Sanjay Seenivasan, Matthew Johnson-Roberson +1Imitation Learning Reverse
College of Connected Computing, Vanderbilt University, USA. · School of Computer Science, University of Waterloo, Canada. · School of Computer Science, The University of Sydney, Australia. +1
Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol Effects
Jan 16, 2026Jose Sánchez AndreuFrozen Encoder Anomaly Score
Independent Researcher, Murcia, Spain
Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Design
Nov 28, 2025Pankhil Gawade, Adam Izdebski, Myriam Lizotte +4Generative Pretrained Transformers Behavioral Embeddings
Institute of AI for Health, Helmholtz Munich · School of Computation, Information and Technology, Technical University of Munich · Mila - Quebec AI institute; Univ. de Montreal +3
Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research
Nov 25, 2025Ciro Benito Raggio, Lucia Migliorelli, Nils Skupien +6Federated Learning Artificial Intelligence Lifecycle
Institute of Biomedical Engineering Karlsruhe Institute of Technology Fritz-Haber-Weg 1, Karlsruhe 76131 Baden-Württemberg, Germany · Department of Political Science Università Degli Studi Di Teramo Via Renato Balizarini 1, Teramo 64100 Italy · Department of Radiation Oncology University Medical Center Schleswig-Holstein Feldstrasse 21, Kiel 24105 Schleswig-Holstein, Germany +1
TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification
Date pendingZe Chen, Qiming Yu, Zijia Song +2Network Traffic Diffusion Models
National University of Defense Technology Hefei, Anhui, China

Apr 24, 2026, cs.CVJ/K move · Enter open · S save

FILTR: Extracting Topological Features from Pretrained 3D Models

Louis Martinez, Maks Ovsjanikov

LIX, ´Ecole Polytechnique, IP Paris

Point Clouds Topology Frozen Encoder Bert-Based Models

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Recent advances in pretraining 3D point cloud encoders (e.g., Point-BERT, Point-MAE) have produced powerful models, whose abilities are typically evaluated on geometric or semantic tasks. At the same time, topological descriptors have been shown to provide informative summaries of a shape's multiscale structure. In this paper we pose the question whether topological information can be derived from features produced by 3D encoders. To address this question, we first introduce DONUT, a synthetic benchmark with controlled topological complexity, and propose FILTR (Filtration Transformer), a learnable framework to predict persistence diagrams directly from frozen encoders. FILTR adapts a transformer decoder to treat diagram generation as a set prediction task. Our analysis on DONUT reveals that existing encoders retain only limited global topological signals, yet FILTR successfully leverages information produced by these encoders to approximate persistence diagrams. Our approach enables, for the first time, data-driven extraction of persistence diagrams from raw point clouds through an efficient learnable feed-forward mechanism.

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Latest papers 66

FILTR: Extracting Topological Features from Pretrained 3D Models

Rewind-IL: Online Failure Detection and State Respawning for Imitation Learning

Auditing Frozen-Encoder Anomaly Detection Across Mechanical Systems: Representation Provenance, Calibration, and Protocol Effects

Freeze, Diffuse, Decode: Task-Aware Adaptation of Transformer Embeddings for Antimicrobial Peptide Design

Energy-Efficient Federated Learning via Adaptive Encoder Freezing for MRI-to-CT Conversion: A Green AI-Guided Research

TDDM-Melatt: A Decoupled Memory and Diffusion Framework for Generalizable Encrypted Traffic Classification