Fully Test-time Adaptation for Tabular Data

• URL: http://arxiv.org/abs/2412.10871v1
• Date: Sat, 14 Dec 2024 15:49:53 GMT
• Title: Fully Test-time Adaptation for Tabular Data
• Authors: Zhi Zhou, Kun-Yang Yu, Lan-Zhe Guo, Yu-Feng Li,
• Abstract summary: We propose the Fully Test-time Adaptation for Tabular data, which enables FTTA methods to robustly optimize the label distribution of predictions. We conduct comprehensive experiments on six benchmark datasets, which are evaluated using three metrics.
• Score: 48.67303250592189
• License:
• Abstract: Tabular data plays a vital role in various real-world scenarios and finds extensive applications. Although recent deep tabular models have shown remarkable success, they still struggle to handle data distribution shifts, leading to performance degradation when testing distributions change. To remedy this, a robust tabular model must adapt to generalize to unknown distributions during testing. In this paper, we investigate the problem of fully test-time adaptation (FTTA) for tabular data, where the model is adapted using only the testing data. We identify three key challenges: the existence of label and covariate distribution shifts, the lack of effective data augmentation, and the sensitivity of adaptation, which render existing FTTA methods ineffective for tabular data. To this end, we propose the Fully Test-time Adaptation for Tabular data, namely FTAT, which enables FTTA methods to robustly optimize the label distribution of predictions, adapt to shifted covariate distributions, and suit a variety of tasks and models effectively. We conduct comprehensive experiments on six benchmark datasets, which are evaluated using three metrics. The experimental results demonstrate that FTAT outperforms state-of-the-art methods by a margin.

Related papers

• Distribution Alignment for Fully Test-Time Adaptation with Dynamic Online Data Streams [19.921480334048756]
  Test-Time Adaptation (TTA) enables adaptation and inference in test data streams with domain shifts from the source. We propose a novel Distribution Alignment loss for TTA. We surpass existing methods in non-i.i.d. scenarios and maintain competitive performance under the ideal i.i.d. assumption.
  arXiv  Detail & Related papers  (2024-07-16T19:33:23Z)
• AdapTable: Test-Time Adaptation for Tabular Data via Shift-Aware Uncertainty Calibrator and Label Distribution Handler [29.395855812763617]
  We propose AdapTable, a framework for adapting machine learning models to target data without accessing source data. AdapTable operates in two stages: 1) calibrating model predictions using a shift-aware uncertainty calibrator, and 2) adjusting these predictions to match the target label distribution with a label distribution handler. Our results demonstrate AdapTable's ability to handle various real-world distribution shifts, achieving up to a 16% improvement on the dataset.
  arXiv  Detail & Related papers  (2024-07-15T15:02:53Z)
• Generalized Robust Test-Time Adaptation in Continuous Dynamic Scenarios [18.527640606971563]
  Test-time adaptation (TTA) adapts pre-trained models to test distributions during the inference phase exclusively employing unlabeled test data streams. We propose a Generalized Robust Test-Time Adaptation (GRoTTA) method to effectively address the difficult problem.
  arXiv  Detail & Related papers  (2023-10-07T07:13:49Z)
• Diverse Data Augmentation with Diffusions for Effective Test-time Prompt Tuning [73.75282761503581]
  We propose DiffTPT, which leverages pre-trained diffusion models to generate diverse and informative new data. Our experiments on test datasets with distribution shifts and unseen categories demonstrate that DiffTPT improves the zero-shot accuracy by an average of 5.13%.
  arXiv  Detail & Related papers  (2023-08-11T09:36:31Z)
• On Pitfalls of Test-Time Adaptation [82.8392232222119]
  Test-Time Adaptation (TTA) has emerged as a promising approach for tackling the robustness challenge under distribution shifts. We present TTAB, a test-time adaptation benchmark that encompasses ten state-of-the-art algorithms, a diverse array of distribution shifts, and two evaluation protocols.
  arXiv  Detail & Related papers  (2023-06-06T09:35:29Z)
• A Comprehensive Survey on Test-Time Adaptation under Distribution Shifts [117.72709110877939]
  Test-time adaptation (TTA) has the potential to adapt a pre-trained model to unlabeled data during testing, before making predictions. We categorize TTA into several distinct groups based on the form of test data, namely, test-time domain adaptation, test-time batch adaptation, and online test-time adaptation.
  arXiv  Detail & Related papers  (2023-03-27T16:32:21Z)
• Robust Test-Time Adaptation in Dynamic Scenarios [9.475271284789969]
  Test-time adaptation (TTA) intends to adapt the pretrained model to test distributions with only unlabeled test data streams. We elaborate a Robust Test-Time Adaptation (RoTTA) method against the complex data stream in PTTA. Our method is easy to implement, making it a good choice for rapid deployment.
  arXiv  Detail & Related papers  (2023-03-24T10:19:14Z)
• CAFA: Class-Aware Feature Alignment for Test-Time Adaptation [50.26963784271912]
  Test-time adaptation (TTA) aims to address this challenge by adapting a model to unlabeled data at test time. We propose a simple yet effective feature alignment loss, termed as Class-Aware Feature Alignment (CAFA), which simultaneously encourages a model to learn target representations in a class-discriminative manner.
  arXiv  Detail & Related papers  (2022-06-01T03:02:07Z)
• Efficient Test-Time Model Adaptation without Forgetting [60.36499845014649]
  Test-time adaptation seeks to tackle potential distribution shifts between training and testing data. We propose an active sample selection criterion to identify reliable and non-redundant samples. We also introduce a Fisher regularizer to constrain important model parameters from drastic changes.
  arXiv  Detail & Related papers  (2022-04-06T06:39:40Z)

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.