Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses

• URL: http://arxiv.org/abs/2304.12858v4
• Date: Thu, 10 Oct 2024 15:48:42 GMT
• Title: Unpaired Image Translation to Mitigate Domain Shift in Liquid Argon Time Projection Chamber Detector Responses
• Authors: Yi Huang, Dmitrii Torbunov, Brett Viren, Haiwang Yu, Jin Huang, Meifeng Lin, Yihui Ren,
• Abstract summary: The " domain shift problem" is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image translation techniques, designed to find translations between different image domains in a fully unsupervised fashion.
• Score: 5.799883835053843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning algorithms often are trained and deployed on different datasets. Any systematic difference between the training and a test dataset may degrade the algorithm performance--what is known as the domain shift problem. This issue is prevalent in many scientific domains where algorithms are trained on simulated data but applied to real-world datasets. Typically, the domain shift problem is solved through various domain adaptation methods. However, these methods are often tailored for a specific downstream task and may not easily generalize to different tasks. This work explores the feasibility of using an alternative way to solve the domain shift problem that is not specific to any downstream algorithm. The proposed approach relies on modern Unpaired Image-to-Image translation techniques, designed to find translations between different image domains in a fully unsupervised fashion. In this study, the approach is applied to a domain shift problem commonly encountered in Liquid Argon Time Projection Chamber (LArTPC) detector research when seeking a way to translate samples between two differently distributed detector datasets deterministically. This translation allows for mapping real-world data into the simulated data domain where the downstream algorithms can be run with much less domain-shift-related degradation. Conversely, using the translation from the simulated data in a real-world domain can increase the realism of the simulated dataset and reduce the magnitude of any systematic uncertainties. We adapted several UI2I translation algorithms to work on scientific data and demonstrated the viability of these techniques for solving the domain shift problem with LArTPC detector data. To facilitate further development of domain adaptation techniques for scientific datasets, the "Simple Liquid-Argon Track Samples" dataset used in this study also is published.

Related papers

• Simulations of Common Unsupervised Domain Adaptation Algorithms for Image Classification [14.98782101639076]
  Domain adaptation (DA) is a machine learning technique that aims to address this problem by reducing the differences between domains. This paper presents simulation-based algorithms of recent DA techniques, mainly related to unsupervised domain adaptation (UDA) Our study compares these techniques with public data sets and diverse characteristics, highlighting their respective strengths and drawbacks.
  arXiv  Detail & Related papers  (2025-02-15T06:58:57Z)
• Multi-Source and Test-Time Domain Adaptation on Multivariate Signals using Spatio-Temporal Monge Alignment [59.75420353684495]
  Machine learning applications on signals such as computer vision or biomedical data often face challenges due to the variability that exists across hardware devices or session recordings. In this work, we propose Spatio-Temporal Monge Alignment (STMA) to mitigate these variabilities. We show that STMA leads to significant and consistent performance gains between datasets acquired with very different settings.
  arXiv  Detail & Related papers  (2024-07-19T13:33:38Z)
• T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds [2.5291108878852864]
  unsupervised domain adaptation (UDA) methods adapt models trained on one (source) domain with annotations available to another (target) domain for which only unannotated data are available. We introduce a novel domain adaptation method that leverages the best of both trends. Dubbed T-UDA for "temporal UDA", such a combination yields massive performance gains for the task of 3D semantic segmentation of driving scenes.
  arXiv  Detail & Related papers  (2023-09-15T10:47:12Z)
• SALUDA: Surface-based Automotive Lidar Unsupervised Domain Adaptation [62.889835139583965]
  We introduce an unsupervised auxiliary task of learning an implicit underlying surface representation simultaneously on source and target data. As both domains share the same latent representation, the model is forced to accommodate discrepancies between the two sources of data. Our experiments demonstrate that our method achieves a better performance than the current state of the art, both in real-to-real and synthetic-to-real scenarios.
  arXiv  Detail & Related papers  (2023-04-06T17:36:23Z)
• One-Shot Domain Adaptive and Generalizable Semantic Segmentation with Class-Aware Cross-Domain Transformers [96.51828911883456]
  Unsupervised sim-to-real domain adaptation (UDA) for semantic segmentation aims to improve the real-world test performance of a model trained on simulated data. Traditional UDA often assumes that there are abundant unlabeled real-world data samples available during training for the adaptation. We explore the one-shot unsupervised sim-to-real domain adaptation (OSUDA) and generalization problem, where only one real-world data sample is available.
  arXiv  Detail & Related papers  (2022-12-14T15:54:15Z)
• Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift [3.071136270246468]
  This paper proposes a novel supervised domain adaptation based on two steps. First, we search for an optimal class-dependent transformation from the source to the target domain from a few samples. Second, we use embedding similarity techniques to select the corresponding transformation at inference.
  arXiv  Detail & Related papers  (2022-04-07T10:27:14Z)
• Towards Unsupervised Domain Adaptation via Domain-Transformer [0.0]
  We propose the Domain-Transformer (DoT) for Unsupervised Domain Adaptation (UDA) DoT integrates the CNN-backbones and the core attention mechanism of Transformers from a new perspective. It achieves the local semantic consistency across domains, where the domain-level attention and manifold regularization are explored.
  arXiv  Detail & Related papers  (2022-02-24T02:30:15Z)
• Achieving Domain Generalization in Underwater Object Detection by Image Stylization and Domain Mixup [8.983901488753967]
  Existing underwater object detection methods degrade seriously when facing domain shift problem caused by complicated underwater environments. We propose a domain generalization method from the aspect of data augmentation. Comprehensive experiments on S-UODAC 2020 datasets demonstrate that the proposed method is able to learn domain-invariant representations.
  arXiv  Detail & Related papers  (2021-04-06T01:45:07Z)
• Flexible deep transfer learning by separate feature embeddings and manifold alignment [0.0]
  Object recognition is a key enabler across industry and defense. Unfortunately, algorithms trained on existing labeled datasets do not directly generalize to new data because the data distributions do not match. We propose a novel deep learning framework that overcomes this limitation by learning separate feature extractions for each domain.
  arXiv  Detail & Related papers  (2020-12-22T19:24:44Z)
• A Review of Single-Source Deep Unsupervised Visual Domain Adaptation [81.07994783143533]
  Large-scale labeled training datasets have enabled deep neural networks to excel across a wide range of benchmark vision tasks. In many applications, it is prohibitively expensive and time-consuming to obtain large quantities of labeled data. To cope with limited labeled training data, many have attempted to directly apply models trained on a large-scale labeled source domain to another sparsely labeled or unlabeled target domain.
  arXiv  Detail & Related papers  (2020-09-01T00:06:50Z)
• Learning Domain-invariant Graph for Adaptive Semi-supervised Domain Adaptation with Few Labeled Source Samples [65.55521019202557]
  Domain adaptation aims to generalize a model from a source domain to tackle tasks in a related but different target domain. Traditional domain adaptation algorithms assume that enough labeled data, which are treated as the prior knowledge are available in the source domain. We propose a Domain-invariant Graph Learning (DGL) approach for domain adaptation with only a few labeled source samples.
  arXiv  Detail & Related papers  (2020-08-21T08:13:25Z)
• Spatial Attention Pyramid Network for Unsupervised Domain Adaptation [66.75008386980869]
  Unsupervised domain adaptation is critical in various computer vision tasks. We design a new spatial attention pyramid network for unsupervised domain adaptation. Our method performs favorably against the state-of-the-art methods by a large margin.
  arXiv  Detail & Related papers  (2020-03-29T09:03:23Z)

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.