Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation

• URL: http://arxiv.org/abs/2409.04409v1
• Date: Fri, 6 Sep 2024 17:13:14 GMT
• Title: Train Till You Drop: Towards Stable and Robust Source-free Unsupervised 3D Domain Adaptation
• Authors: Björn Michele, Alexandre Boulch, Tuan-Hung Vu, Gilles Puy, Renaud Marlet, Nicolas Courty,
• Abstract summary: We tackle the problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled target domain without any access to source data. A common issue with existing SFUDA approaches is that performance degrades after some training time.
• Score: 62.889835139583965
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We tackle the challenging problem of source-free unsupervised domain adaptation (SFUDA) for 3D semantic segmentation. It amounts to performing domain adaptation on an unlabeled target domain without any access to source data; the available information is a model trained to achieve good performance on the source domain. A common issue with existing SFUDA approaches is that performance degrades after some training time, which is a by product of an under-constrained and ill-posed problem. We discuss two strategies to alleviate this issue. First, we propose a sensible way to regularize the learning problem. Second, we introduce a novel criterion based on agreement with a reference model. It is used (1) to stop the training when appropriate and (2) as validator to select hyperparameters without any knowledge on the target domain. Our contributions are easy to implement and readily amenable for all SFUDA methods, ensuring stable improvements over all baselines. We validate our findings on various 3D lidar settings, achieving state-of-the-art performance. The project repository (with code) is: github.com/valeoai/TTYD.

Related papers

• Dynamic Retraining-Updating Mean Teacher for Source-Free Object Detection [8.334498654271371]
  Unsupervised domain adaptation (UDA) aims to transfer knowledge from a labeled source domain to an unlabeled target domain. This study focuses on source-free object detection (SFOD), which adapts a source-trained detector to an unlabeled target domain without using labeled source data.
  arXiv  Detail & Related papers  (2024-07-23T14:12:57Z)
• Backpropagation-free Network for 3D Test-time Adaptation [42.469853469556966]
  Test-Time Adaptation (TTA) methods tend to apply computationally heavy and memory-intensive backpropagation-based approaches. Here, we propose a novel method that uses a backpropagation-free approach for TTA for the specific case of 3D data.
  arXiv  Detail & Related papers  (2024-03-27T10:50:24Z)
• SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point Cloud [125.9472454212909]
  We present a novel Semi-Supervised Domain Adaptation method for 3D object detection (SSDA3D) SSDA3D includes an Inter-domain Adaptation stage and an Intra-domain Generalization stage. Experiments show that, with only 10% labeled target data, our SSDA3D can surpass the fully-supervised oracle model with 100% target label.
  arXiv  Detail & Related papers  (2022-12-06T09:32:44Z)
• Domain Adaptive Person Search [20.442648584402917]
  We present Domain Adaptive Person Search (DAPS), which aims to generalize the model from a labeled source domain to the unlabeled target domain. We show that our framework achieves 34.7% in mAP and 80.6% in top-1 on PRW dataset.
  arXiv  Detail & Related papers  (2022-07-25T04:02:39Z)
• Prior Knowledge Guided Unsupervised Domain Adaptation [82.9977759320565]
  We propose a Knowledge-guided Unsupervised Domain Adaptation (KUDA) setting where prior knowledge about the target class distribution is available. In particular, we consider two specific types of prior knowledge about the class distribution in the target domain: Unary Bound and Binary Relationship. We propose a rectification module that uses such prior knowledge to refine model generated pseudo labels.
  arXiv  Detail & Related papers  (2022-07-18T18:41:36Z)
• Target and Task specific Source-Free Domain Adaptive Image Segmentation [73.78898054277538]
  We propose a two-stage approach for source-free domain adaptive image segmentation. We focus on generating target-specific pseudo labels while suppressing high entropy regions. In the second stage, we focus on adapting the network for task-specific representation.
  arXiv  Detail & Related papers  (2022-03-29T17:50:22Z)
• Continual Test-Time Domain Adaptation [94.51284735268597]
  Test-time domain adaptation aims to adapt a source pre-trained model to a target domain without using any source data. CoTTA is easy to implement and can be readily incorporated in off-the-shelf pre-trained models.
  arXiv  Detail & Related papers  (2022-03-25T11:42:02Z)
• Do We Really Need to Access the Source Data? Source Hypothesis Transfer for Unsupervised Domain Adaptation [102.67010690592011]
  Unsupervised adaptationUDA (UDA) aims to leverage the knowledge learned from a labeled source dataset to solve similar tasks in a new unlabeled domain. Prior UDA methods typically require to access the source data when learning to adapt the model. This work tackles a practical setting where only a trained source model is available and how we can effectively utilize such a model without source data to solve UDA problems.
  arXiv  Detail & Related papers  (2020-02-20T03:13:58Z)

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.