SITA: Single Image Test-time Adaptation

• URL: http://arxiv.org/abs/2112.02355v2
• Date: Wed, 8 Dec 2021 09:18:18 GMT
• Title: SITA: Single Image Test-time Adaptation
• Authors: Ansh Khurana, Sujoy Paul, Piyush Rai, Soma Biswas, Gaurav Aggarwal
• Abstract summary: In Test-time Adaptation (TTA), given a model trained on some source data, the goal is to adapt it to make better predictions for test instances from a different distribution. We consider TTA in a more pragmatic setting which we refer to as SITA (Single Image Test-time Adaptation) Here, when making each prediction, the model has access only to the given single test instance, rather than a batch of instances. We propose a novel approach AugBN for the SITA setting that requires only forward-preserving propagation.
• Score: 48.789568233682296
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In Test-time Adaptation (TTA), given a model trained on some source data, the goal is to adapt it to make better predictions for test instances from a different distribution. Crucially, TTA assumes no access to the source data or even any additional labeled/unlabeled samples from the target distribution to finetune the source model. In this work, we consider TTA in a more pragmatic setting which we refer to as SITA (Single Image Test-time Adaptation). Here, when making each prediction, the model has access only to the given single test instance, rather than a batch of instances, as has typically been considered in the literature. This is motivated by the realistic scenarios where inference is needed in an on-demand fashion that may not be delayed to "batch-ify" incoming requests or the inference is happening on an edge device (like mobile phone) where there is no scope for batching. The entire adaptation process in SITA should be extremely fast as it happens at inference time. To address this, we propose a novel approach AugBN for the SITA setting that requires only forward propagation. The approach can adapt any off-the-shelf trained model to individual test instances for both classification and segmentation tasks. AugBN estimates normalisation statistics of the unseen test distribution from the given test image using only one forward pass with label-preserving transformations. Since AugBN does not involve any back-propagation, it is significantly faster compared to other recent methods. To the best of our knowledge, this is the first work that addresses this hard adaptation problem using only a single test image. Despite being very simple, our framework is able to achieve significant performance gains compared to directly applying the source model on the target instances, as reflected in our extensive experiments and ablation studies.

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