Related papers: Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need

URL: http://arxiv.org/abs/2410.03644v1
Date: Fri, 4 Oct 2024 17:49:32 GMT
Title: Unlearnable 3D Point Clouds: Class-wise Transformation Is All You Need
Authors: Xianlong Wang, Minghui Li, Wei Liu, Hangtao Zhang, Shengshan Hu, Yechao Zhang, Ziqi Zhou, Hai Jin,
Abstract summary: Unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. We propose the first integral unlearnable framework for 3D point clouds including two processes. Both theoretical and empirical results demonstrate the effectiveness of our proposed unlearnable framework.
Score: 24.18942067770636
License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
Abstract: Traditional unlearnable strategies have been proposed to prevent unauthorized users from training on the 2D image data. With more 3D point cloud data containing sensitivity information, unauthorized usage of this new type data has also become a serious concern. To address this, we propose the first integral unlearnable framework for 3D point clouds including two processes: (i) we propose an unlearnable data protection scheme, involving a class-wise setting established by a category-adaptive allocation strategy and multi-transformations assigned to samples; (ii) we propose a data restoration scheme that utilizes class-wise inverse matrix transformation, thus enabling authorized-only training for unlearnable data. This restoration process is a practical issue overlooked in most existing unlearnable literature, \ie, even authorized users struggle to gain knowledge from 3D unlearnable data. Both theoretical and empirical results (including 6 datasets, 16 models, and 2 tasks) demonstrate the effectiveness of our proposed unlearnable framework. Our code is available at \url{https://github.com/CGCL-codes/UnlearnablePC}

Related papers

Toward Availability Attacks in 3D Point Clouds [28.496421433836908]
We show that extending 2D availability attacks directly to 3D point clouds under distance regularization is susceptible to the degeneracy. We propose a novel Feature Collision Error-Minimization (FC-EM) method, which creates additional shortcuts in the feature space. Experiments on typical point cloud datasets, 3D intracranial aneurysm medical dataset, and 3D face dataset verify the superiority and practicality of our approach.
arXiv Detail & Related papers (2024-06-26T08:13:30Z)
Robust and Explainable Fine-Grained Visual Classification with Transfer Learning: A Dual-Carriageway Framework [0.799543372823325]
We present an automatic best-suit training solution searching framework, the Dual-Carriageway Framework (DCF) We validated DCF's effectiveness through experiments with three convolutional neural networks (ResNet18, ResNet34 and Inception-v3) Results showed fine-tuning pathways outperformed training-from-scratch ones by up to 2.13% and 1.23% on the pre-existing and new datasets, respectively.
arXiv Detail & Related papers (2024-05-09T15:41:10Z)
Class-Imbalanced Semi-Supervised Learning for Large-Scale Point Cloud Semantic Segmentation via Decoupling Optimization [64.36097398869774]
Semi-supervised learning (SSL) has been an active research topic for large-scale 3D scene understanding. The existing SSL-based methods suffer from severe training bias due to class imbalance and long-tail distributions of the point cloud data. We introduce a new decoupling optimization framework, which disentangles feature representation learning and classifier in an alternative optimization manner to shift the bias decision boundary effectively.
arXiv Detail & Related papers (2024-01-13T04:16:40Z)
What Can We Learn from Unlearnable Datasets? [107.12337511216228]
Unlearnable datasets have the potential to protect data privacy by preventing deep neural networks from generalizing. It is widely believed that neural networks trained on unlearnable datasets only learn shortcuts, simpler rules that are not useful for generalization. In contrast, we find that networks actually can learn useful features that can be reweighed for high test performance, suggesting that image protection is not assured.
arXiv Detail & Related papers (2023-05-30T17:41:35Z)
Few-Shot Non-Parametric Learning with Deep Latent Variable Model [50.746273235463754]
We propose Non-Parametric learning by Compression with Latent Variables (NPC-LV) NPC-LV is a learning framework for any dataset with abundant unlabeled data but very few labeled ones. We show that NPC-LV outperforms supervised methods on all three datasets on image classification in low data regime.
arXiv Detail & Related papers (2022-06-23T09:35:03Z)
Open-Set Semi-Supervised Learning for 3D Point Cloud Understanding [62.17020485045456]
It is commonly assumed in semi-supervised learning (SSL) that the unlabeled data are drawn from the same distribution as that of the labeled ones. We propose to selectively utilize unlabeled data through sample weighting, so that only conducive unlabeled data would be prioritized.
arXiv Detail & Related papers (2022-05-02T16:09:17Z)
Machine Unlearning of Features and Labels [72.81914952849334]
We propose first scenarios for unlearning and labels in machine learning models. Our approach builds on the concept of influence functions and realizes unlearning through closed-form updates of model parameters.
arXiv Detail & Related papers (2021-08-26T04:42:24Z)
SelfVoxeLO: Self-supervised LiDAR Odometry with Voxel-based Deep Neural Networks [81.64530401885476]
We propose a self-supervised LiDAR odometry method, dubbed SelfVoxeLO, to tackle these two difficulties. Specifically, we propose a 3D convolution network to process the raw LiDAR data directly, which extracts features that better encode the 3D geometric patterns. We evaluate our method's performances on two large-scale datasets, i.e., KITTI and Apollo-SouthBay.
arXiv Detail & Related papers (2020-10-19T09:23:39Z)

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