MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption

• URL: http://arxiv.org/abs/2103.16201v1
• Date: Tue, 30 Mar 2021 09:33:38 GMT
• Title: MT3: Meta Test-Time Training for Self-Supervised Test-Time Adaption
• Authors: Alexander Bartler, Andre B\"uhler, Felix Wiewel, Mario D\"obler and Bin Yang
• Abstract summary: An unresolved problem in Deep Learning is the ability of neural networks to cope with domain shifts during test-time. We combine meta-learning, self-supervision and test-time training to learn to adapt to unseen test distributions. Our approach significantly improves the state-of-the-art results on the CIFAR-10-Corrupted image classification benchmark.
• Score: 69.76837484008033
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An unresolved problem in Deep Learning is the ability of neural networks to cope with domain shifts during test-time, imposed by commonly fixing network parameters after training. Our proposed method Meta Test-Time Training (MT3), however, breaks this paradigm and enables adaption at test-time. We combine meta-learning, self-supervision and test-time training to learn to adapt to unseen test distributions. By minimizing the self-supervised loss, we learn task-specific model parameters for different tasks. A meta-model is optimized such that its adaption to the different task-specific models leads to higher performance on those tasks. During test-time a single unlabeled image is sufficient to adapt the meta-model parameters. This is achieved by minimizing only the self-supervised loss component resulting in a better prediction for that image. Our approach significantly improves the state-of-the-art results on the CIFAR-10-Corrupted image classification benchmark. Our implementation is available on GitHub.

Related papers

• Adaptive Cascading Network for Continual Test-Time Adaptation [12.718826132518577]
  We study the problem of continual test-time adaption where the goal is to adapt a source pre-trained model to a sequence of unlabelled target domains at test time. Existing methods on test-time training suffer from several limitations.
  arXiv  Detail & Related papers  (2024-07-17T01:12:57Z)
• Incorporating Test-Time Optimization into Training with Dual Networks for Human Mesh Recovery [35.138312681232264]
  We propose a dual-network architecture that unifies the training-time and test-time objectives. Our method, armed with meta-learning and the dual networks, outperforms state-of-the-art regression-based and optimization-based HMR approaches.
  arXiv  Detail & Related papers  (2024-01-25T12:04:53Z)
• Point-TTA: Test-Time Adaptation for Point Cloud Registration Using Multitask Meta-Auxiliary Learning [17.980649681325406]
  We present Point-TTA, a novel test-time adaptation framework for point cloud registration (PCR) Our model can adapt to unseen distributions at test-time without requiring any prior knowledge of the test data. During training, our model is trained using a meta-auxiliary learning approach, such that the adapted model via auxiliary tasks improves the accuracy of the primary task.
  arXiv  Detail & Related papers  (2023-08-31T06:32:11Z)
• Architecture, Dataset and Model-Scale Agnostic Data-free Meta-Learning [119.70303730341938]
  We propose ePisode cUrriculum inveRsion (ECI) during data-free meta training and invErsion calibRation following inner loop (ICFIL) during meta testing. ECI adaptively increases the difficulty level of pseudo episodes according to the real-time feedback of the meta model. We formulate the optimization process of meta training with ECI as an adversarial form in an end-to-end manner.
  arXiv  Detail & Related papers  (2023-03-20T15:10:41Z)
• Robust Meta Learning for Image based tasks [1.1718589131017048]
  A machine learning model that generalizes well should obtain low errors on unseen test examples. We propose a novel robust meta-learning method, which is more robust to the image-based testing tasks. In experiments, we demonstrate that our algorithm not only has better generalization performance but also robust to different unknown testing tasks.
  arXiv  Detail & Related papers  (2023-01-30T07:08:37Z)
• MATE: Masked Autoencoders are Online 3D Test-Time Learners [63.3907730920114]
  MATE is the first Test-Time-Training (TTT) method designed for 3D data. It makes deep networks trained for point cloud classification robust to distribution shifts occurring in test data.
  arXiv  Detail & Related papers  (2022-11-21T13:19:08Z)
• TeST: Test-time Self-Training under Distribution Shift [99.68465267994783]
  Test-Time Self-Training (TeST) is a technique that takes as input a model trained on some source data and a novel data distribution at test time. We find that models adapted using TeST significantly improve over baseline test-time adaptation algorithms.
  arXiv  Detail & Related papers  (2022-09-23T07:47:33Z)
• 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)
• On-the-Fly Test-time Adaptation for Medical Image Segmentation [63.476899335138164]
  Adapting the source model to target data distribution at test-time is an efficient solution for the data-shift problem. We propose a new framework called Adaptive UNet where each convolutional block is equipped with an adaptive batch normalization layer. During test-time, the model takes in just the new test image and generates a domain code to adapt the features of source model according to the test data.
  arXiv  Detail & Related papers  (2022-03-10T18:51:29Z)

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.