Purge-Gate: Backpropagation-Free Test-Time Adaptation for Point Clouds Classification via Token Purging

• URL: http://arxiv.org/abs/2509.09785v1
• Date: Thu, 11 Sep 2025 18:33:40 GMT
• Title: Purge-Gate: Backpropagation-Free Test-Time Adaptation for Point Clouds Classification via Token Purging
• Abstract summary: Token Purging (PG) is a backpropagation-free approach that removes tokens highly affected by domain shifts before they reach attention layers.<n>We propose two variants: PG-SP, which leverages source statistics, and PG-SF, a fully source-free version relying on CLS-token-driven adaptation.<n>PG-SP achieves an average of +10.3% higher accuracy than state-of-the-art backpropagation-free methods, while PG-SF sets new benchmarks for source-free adaptation.
• Score: 33.86980091897939
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Test-time adaptation (TTA) is crucial for mitigating performance degradation caused by distribution shifts in 3D point cloud classification. In this work, we introduce Token Purging (PG), a novel backpropagation-free approach that removes tokens highly affected by domain shifts before they reach attention layers. Unlike existing TTA methods, PG operates at the token level, ensuring robust adaptation without iterative updates. We propose two variants: PG-SP, which leverages source statistics, and PG-SF, a fully source-free version relying on CLS-token-driven adaptation. Extensive evaluations on ModelNet40-C, ShapeNet-C, and ScanObjectNN-C demonstrate that PG-SP achieves an average of +10.3\% higher accuracy than state-of-the-art backpropagation-free methods, while PG-SF sets new benchmarks for source-free adaptation. Moreover, PG is 12.4 times faster and 5.5 times more memory efficient than our baseline, making it suitable for real-world deployment. Code is available at \hyperlink{https://github.com/MosyMosy/Purge-Gate}{https://github.com/MosyMosy/Purge-Gate}

Related papers

• SS-ESOAP: Self-Scaled Adaptive Preconditioning for Physics-Informed Learning [70.71795707610961]
  method augments SOAP-style preconditioning with a scalar secant-energy correction adapted to Kronecker geometry.<n>Method attains the lowest final residual on six of eight PDE benchmarks.<n>Three-seed $L2$ and $H1$ errors on four representative PDEs support the link between lower residuals and improved solution accuracy.
  arXiv  Detail & Related papers  (2026-08-29T21:34:47Z)
• When Test-Time Adaptation Helps, Harms, or Becomes Inactive: A Condition-Level Study on CIFAR-10-C [0.0]
  Test-time adaptation (TTA) aims to improve model robustness under distribution shift by adapting a source model using unlabeled test data.<n>We present a comparison of three TTA strategies---BatchNorm-statistics adaptation (BN-Adapt), entropy-minimization adaptation (TENT), and reliability-statistics adaptation ( EATA)<n>All three methods improve mean accuracy over the source model by 12.2--13.3 percentage points (Wilcoxon signed-rank $p 10-12$)
  arXiv  Detail & Related papers  (2026-08-23T06:06:38Z)
• Point-Selection Fine-Tuning Framework for Robust Point Cloud Classification [13.13510696965873]
  PSFT is a point-selection fine-tuning framework that improves robustness while remaining parameter-efficient.<n>We show that PSFT consistently reduces corruption error on Model-C and ModelNet40-C across all tested 3D pre-trained backbones.
  arXiv  Detail & Related papers  (2026-07-22T03:27:18Z)
• The Golden Subspace: Where Efficiency Meets Generalization in Continual Test-Time Adaptation [86.83038620308423]
  Continual Test-Time Adaptation (CTTA) aims to enable models to adapt online to unlabeled data streams under distribution shift without accessing source data.<n>We propose Guided Online Low-rank Directional adaptation (GOLD), which uses a lightweight adapter to project features onto the golden subspace and learns a compact scaling vector while the subspace is dynamically updated via AGOP.
  arXiv  Detail & Related papers  (2026-03-23T12:48:38Z)
• CORE: Context-Robust Remasking for Diffusion Language Models [51.59514489363897]
  We propose Context-Robust Remasking (CORE), a training-free framework for inference-time revision.<n>Rather than trusting static token probabilities, CORE identifies context-brittle tokens by probing their sensitivity to targeted masked-context perturbations.<n>On LLaDA-8B-Base, CORE delivers consistent improvements across reasoning and code benchmarks, outperforming compute-matched baselines and improving MBPP by up to 9.2 percentage points.
  arXiv  Detail & Related papers  (2026-02-04T00:12:30Z)
• A3-TTA: Adaptive Anchor Alignment Test-Time Adaptation for Image Segmentation [17.122762119608144]
  Test-Time Adaptation (TTA) offers a practical solution for deploying image segmentation models under domain shift.<n>We propose textbfA3-TTA, a framework that constructs reliable pseudo-labels through anchor-guided supervision.<n>A3-TTA significantly improves average Dice scores by 10.40 to 17.68 percentage points compared to the source model.
  arXiv  Detail & Related papers  (2026-02-03T09:18:11Z)
• SNAP: Low-Latency Test-Time Adaptation with Sparse Updates [31.155455736641116]
  Test-Time Adaptation (TTA) adjusts models using unlabeled test data to handle dynamic distribution shifts.<n>We propose SNAP, a sparse TTA framework that reduces adaptation frequency and data usage while preserving accuracy.<n> SNAP maintains competitive accuracy even when adapting based on only 1% of the incoming data stream.
  arXiv  Detail & Related papers  (2025-11-19T09:40:24Z)
• Ultra-Light Test-Time Adaptation for Vision--Language Models [0.6816905600359814]
  Vision-Language Models (VLMs) such as CLIP achieve strong zero-shot recognition by comparing image embeddings to text-derived class prototypes.<n>Under domain shift, they suffer from feature drift, class-prior mismatch, and severe miscalibration.<n>We propose Ultra-Light Test-Time Adaptation (UL-TTA), a fully training-free and backprop-free framework that freezes the backbone and adapts only logit-level parameters.
  arXiv  Detail & Related papers  (2025-11-12T08:19:57Z)
• SMART-PC: Skeletal Model Adaptation for Robust Test-Time Training in Point Clouds [18.33878596057853]
  Test-Time Training (TTT) has emerged as a promising solution to address distribution shifts in 3D point cloud classification.<n>We introduce SMART-PC, a skeleton-based framework that enhances resilience to corruptions by leveraging the geometric structure of 3D point clouds.
  arXiv  Detail & Related papers  (2025-05-26T06:11:02Z)
• ReservoirTTA: Prolonged Test-time Adaptation for Evolving and Recurring Domains [17.357842682605185]
  ReservoirTTA is a novel plug-in framework designed for prolonged test-time adaptation.<n>At its core, ReservoirTTA maintains a reservoir of domain-specialized models.<n>Our theoretical analysis reveals key components that bound parameter variance and prevent model collapse.
  arXiv  Detail & Related papers  (2025-05-20T15:39:20Z)
• APCoTTA: Continual Test-Time Adaptation for Semantic Segmentation of Airborne LiDAR Point Clouds [14.348191795901101]
  Airborne laser scanning (ALS) point cloud segmentation is a fundamental task for large-scale 3D scene understanding.<n> Continuous Test-Time Adaptation (CTTA) offers a solution by adapting a source-pretrained model to evolving, unlabeled target domains.<n>We propose APCoTTA, the first CTTA method tailored for ALS point cloud semantic segmentation.
  arXiv  Detail & Related papers  (2025-05-15T05:21:16Z)
• Test-Time Model Adaptation with Only Forward Passes [68.11784295706995]
  Test-time adaptation has proven effective in adapting a given trained model to unseen test samples with potential distribution shifts. We propose a test-time Forward-Optimization Adaptation (FOA) method. FOA runs on quantized 8-bit ViT, outperforms gradient-based TENT on full-precision 32-bit ViT, and achieves an up to 24-fold memory reduction on ImageNet-C.
  arXiv  Detail & Related papers  (2024-04-02T05:34:33Z)
• Rethinking Few-shot 3D Point Cloud Semantic Segmentation [62.80639841429669]
  This paper revisits few-shot 3D point cloud semantic segmentation (FS-PCS) We focus on two significant issues in the state-of-the-art: foreground leakage and sparse point distribution. To address these issues, we introduce a standardized FS-PCS setting, upon which a new benchmark is built.
  arXiv  Detail & Related papers  (2024-03-01T15:14:47Z)
• Sparse is Enough in Fine-tuning Pre-trained Large Language Models [98.46493578509039]
  We propose a gradient-based sparse fine-tuning algorithm, named Sparse Increment Fine-Tuning (SIFT) We validate its effectiveness on a range of tasks including the GLUE Benchmark and Instruction-tuning.
  arXiv  Detail & Related papers  (2023-12-19T06:06:30Z)
• PointGPT: Auto-regressively Generative Pre-training from Point Clouds [45.488532108226565]
  We present PointGPT, a novel approach that extends the concept of GPT to point clouds. Specifically, a point cloud auto-regressive generation task is proposed to pre-train transformer models. Our approach achieves classification accuracies of 94.9% on the ModelNet40 dataset and 93.4% on the ScanObjectNN dataset, outperforming all other transformer models.
  arXiv  Detail & Related papers  (2023-05-19T07:39:04Z)
• Post-Processing Temporal Action Detection [134.26292288193298]
  Temporal Action Detection (TAD) methods typically take a pre-processing step in converting an input varying-length video into a fixed-length snippet representation sequence. This pre-processing step would temporally downsample the video, reducing the inference resolution and hampering the detection performance in the original temporal resolution. We introduce a novel model-agnostic post-processing method without model redesign and retraining.
  arXiv  Detail & Related papers  (2022-11-27T19:50:37Z)
• Test-time Batch Statistics Calibration for Covariate Shift [66.7044675981449]
  We propose to adapt the deep models to the novel environment during inference. We present a general formulation $alpha$-BN to calibrate the batch statistics. We also present a novel loss function to form a unified test time adaptation framework Core.
  arXiv  Detail & Related papers  (2021-10-06T08:45:03Z)

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.