SoTTA: Robust Test-Time Adaptation on Noisy Data Streams

• URL: http://arxiv.org/abs/2310.10074v1
• Date: Mon, 16 Oct 2023 05:15:35 GMT
• Title: SoTTA: Robust Test-Time Adaptation on Noisy Data Streams
• Authors: Taesik Gong, Yewon Kim, Taeckyung Lee, Sorn Chottananurak, Sung-Ju Lee
• Abstract summary: Test-time adaptation (TTA) aims to address distributional shifts between training and testing data. Most TTA methods assume benign test streams, while test samples could be unexpectedly diverse in the wild. We present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples.
• Score: 9.490557638324084
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Test-time adaptation (TTA) aims to address distributional shifts between training and testing data using only unlabeled test data streams for continual model adaptation. However, most TTA methods assume benign test streams, while test samples could be unexpectedly diverse in the wild. For instance, an unseen object or noise could appear in autonomous driving. This leads to a new threat to existing TTA algorithms; we found that prior TTA algorithms suffer from those noisy test samples as they blindly adapt to incoming samples. To address this problem, we present Screening-out Test-Time Adaptation (SoTTA), a novel TTA algorithm that is robust to noisy samples. The key enabler of SoTTA is two-fold: (i) input-wise robustness via high-confidence uniform-class sampling that effectively filters out the impact of noisy samples and (ii) parameter-wise robustness via entropy-sharpness minimization that improves the robustness of model parameters against large gradients from noisy samples. Our evaluation with standard TTA benchmarks with various noisy scenarios shows that our method outperforms state-of-the-art TTA methods under the presence of noisy samples and achieves comparable accuracy to those methods without noisy samples. The source code is available at https://github.com/taeckyung/SoTTA .

Related papers

• ETAGE: Enhanced Test Time Adaptation with Integrated Entropy and Gradient Norms for Robust Model Performance [18.055032898349438]
  Test time adaptation (TTA) equips deep learning models to handle unseen test data that deviates from the training distribution. We introduce ETAGE, a refined TTA method that integrates entropy minimization with gradient norms and PLPD. Our method prioritizes samples that are less likely to cause instability by combining high entropy with high gradient norms out of adaptation.
  arXiv  Detail & Related papers  (2024-09-14T01:25:52Z)
• Active Test-Time Adaptation: Theoretical Analyses and An Algorithm [51.84691955495693]
  Test-time adaptation (TTA) addresses distribution shifts for streaming test data in unsupervised settings. We propose the novel problem setting of active test-time adaptation (ATTA) that integrates active learning within the fully TTA setting.
  arXiv  Detail & Related papers  (2024-04-07T22:31:34Z)
• Decoupled Prototype Learning for Reliable Test-Time Adaptation [50.779896759106784]
  Test-time adaptation (TTA) is a task that continually adapts a pre-trained source model to the target domain during inference. One popular approach involves fine-tuning model with cross-entropy loss according to estimated pseudo-labels. This study reveals that minimizing the classification error of each sample causes the cross-entropy loss's vulnerability to label noise. We propose a novel Decoupled Prototype Learning (DPL) method that features prototype-centric loss computation.
  arXiv  Detail & Related papers  (2024-01-15T03:33:39Z)
• 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)
• 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)
• Towards Stable Test-Time Adaptation in Dynamic Wild World [60.98073673220025]
  Test-time adaptation (TTA) has shown to be effective at tackling distribution shifts between training and testing data by adapting a given model on test samples. Online model updating of TTA may be unstable and this is often a key obstacle preventing existing TTA methods from being deployed in the real world.
  arXiv  Detail & Related papers  (2023-02-24T02:03:41Z)
• Robust Continual Test-time Adaptation: Instance-aware BN and Prediction-balanced Memory [58.72445309519892]
  We present a new test-time adaptation scheme that is robust against non-i.i.d. test data streams. Our novelty is mainly two-fold: (a) Instance-Aware Batch Normalization (IABN) that corrects normalization for out-of-distribution samples, and (b) Prediction-balanced Reservoir Sampling (PBRS) that simulates i.i.d. data stream from non-i.i.d. stream in a class-balanced manner.
  arXiv  Detail & Related papers  (2022-08-10T03:05:46Z)
• 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.