---
_id: '14105'
abstract:
- lang: eng
  text: "Despite their recent success, deep neural networks continue to perform poorly
    when they encounter distribution shifts at test time. Many recently proposed approaches
    try to counter this by aligning the model to the new distribution prior to inference.
    With no labels available this requires unsupervised objectives to adapt the model
    on the observed test data. In this paper, we propose Test-Time SelfTraining (TeST):
    a technique that takes as input a model trained on some source data and a novel
    data distribution at test time, and learns invariant and robust representations
    using a student-teacher framework. We find that models adapted using TeST significantly
    improve over baseline testtime adaptation algorithms. TeST achieves competitive
    performance to modern domain adaptation algorithms [4, 43], while having access
    to 5-10x less data at time of adaption. We thoroughly evaluate a variety of baselines
    on two tasks:\r\nobject detection and image segmentation and find that models
    adapted with TeST. We find that TeST sets the new stateof-the art for test-time
    domain adaptation algorithms. "
article_processing_charge: No
arxiv: 1
author:
- first_name: Samarth
  full_name: Sinha, Samarth
  last_name: Sinha
- first_name: Peter
  full_name: Gehler, Peter
  last_name: Gehler
- first_name: Francesco
  full_name: Locatello, Francesco
  id: 26cfd52f-2483-11ee-8040-88983bcc06d4
  last_name: Locatello
  orcid: 0000-0002-4850-0683
- first_name: Bernt
  full_name: Schiele, Bernt
  last_name: Schiele
citation:
  ama: 'Sinha S, Gehler P, Locatello F, Schiele B. TeST: Test-time Self-Training under
    distribution shift. In: <i>2023 IEEE/CVF Winter Conference on Applications of
    Computer Vision</i>. Institute of Electrical and Electronics Engineers; 2023.
    doi:<a href="https://doi.org/10.1109/wacv56688.2023.00278">10.1109/wacv56688.2023.00278</a>'
  apa: 'Sinha, S., Gehler, P., Locatello, F., &#38; Schiele, B. (2023). TeST: Test-time
    Self-Training under distribution shift. In <i>2023 IEEE/CVF Winter Conference
    on Applications of Computer Vision</i>. Waikoloa, HI, United States: Institute
    of Electrical and Electronics Engineers. <a href="https://doi.org/10.1109/wacv56688.2023.00278">https://doi.org/10.1109/wacv56688.2023.00278</a>'
  chicago: 'Sinha, Samarth, Peter Gehler, Francesco Locatello, and Bernt Schiele.
    “TeST: Test-Time Self-Training under Distribution Shift.” In <i>2023 IEEE/CVF
    Winter Conference on Applications of Computer Vision</i>. Institute of Electrical
    and Electronics Engineers, 2023. <a href="https://doi.org/10.1109/wacv56688.2023.00278">https://doi.org/10.1109/wacv56688.2023.00278</a>.'
  ieee: 'S. Sinha, P. Gehler, F. Locatello, and B. Schiele, “TeST: Test-time Self-Training
    under distribution shift,” in <i>2023 IEEE/CVF Winter Conference on Applications
    of Computer Vision</i>, Waikoloa, HI, United States, 2023.'
  ista: 'Sinha S, Gehler P, Locatello F, Schiele B. 2023. TeST: Test-time Self-Training
    under distribution shift. 2023 IEEE/CVF Winter Conference on Applications of Computer
    Vision. WACV: Winter Conference on Applications of Computer Vision.'
  mla: 'Sinha, Samarth, et al. “TeST: Test-Time Self-Training under Distribution Shift.”
    <i>2023 IEEE/CVF Winter Conference on Applications of Computer Vision</i>, Institute
    of Electrical and Electronics Engineers, 2023, doi:<a href="https://doi.org/10.1109/wacv56688.2023.00278">10.1109/wacv56688.2023.00278</a>.'
  short: S. Sinha, P. Gehler, F. Locatello, B. Schiele, in:, 2023 IEEE/CVF Winter
    Conference on Applications of Computer Vision, Institute of Electrical and Electronics
    Engineers, 2023.
conference:
  end_date: 2023-01-07
  location: Waikoloa, HI, United States
  name: 'WACV: Winter Conference on Applications of Computer Vision'
  start_date: 2023-01-02
date_created: 2023-08-21T12:11:38Z
date_published: 2023-02-06T00:00:00Z
date_updated: 2023-09-06T10:26:56Z
day: '06'
department:
- _id: FrLo
doi: 10.1109/wacv56688.2023.00278
extern: '1'
external_id:
  arxiv:
  - '2209.11459'
language:
- iso: eng
main_file_link:
- open_access: '1'
  url: https://arxiv.org/abs/2209.11459
month: '02'
oa: 1
oa_version: Preprint
publication: 2023 IEEE/CVF Winter Conference on Applications of Computer Vision
publication_identifier:
  eissn:
  - 2642-9381
  isbn:
  - '9781665493475'
publication_status: published
publisher: Institute of Electrical and Electronics Engineers
quality_controlled: '1'
scopus_import: '1'
status: public
title: 'TeST: Test-time Self-Training under distribution shift'
type: conference
user_id: 2DF688A6-F248-11E8-B48F-1D18A9856A87
year: '2023'
...