Test-time task adaptation in few-shot learning aims to adapt a pre-trained task-agnostic model for capturing taskspecific knowledge of the test task, rely only on few-labeled support samples. Previous approaches generally focus on developing advanced algorithms to achieve the goal, while neglecting the inherent problems of the given support samples. In fact, with only a handful of samples available, the adverse effect of either the image noise (a.k.a. X-noise) or the label noise (a.k.a. Y-noise) from support samples can be severely amplified. To address this challenge, in this work we propose DEnoised Task Adaptation (DETA), a first, unified image- and label-denoising framework orthogonal to existing task adaptation approaches. Without extra supervision, DETA filters out task-irrelevant, noisy representations by taking advantage of both global visual information and local region details of support samples. On the challenging Meta-Dataset, DETA consistently improves the performance of a broad spectrum of baseline methods applied on various pre-trained models. Notably, by tackling the overlooked image noise in Meta-Dataset, DETA establishes new state-of-the-art results. Code is released at https://github.com/JimZAI/DETA.

翻译：在小样本学习的测试时任务自适应中，旨在针对测试任务仅利用少量标注支持样本，将预训练的任务无关模型进行适配以捕获任务特定知识。现有方法通常聚焦于开发高级算法来实现该目标，却忽视了给定支持样本的固有问题。事实上，在仅有少量样本可用的情况下，支持样本中的图像噪声（即X-噪声）或标签噪声（即Y-噪声）的有害影响会被严重放大。针对这一挑战，本文提出去噪任务自适应（DETA），这是首个且统一的图像与标签去噪框架，与现有任务自适应方法正交。无需额外监督，DETA通过利用支持样本的全局视觉信息与局部区域细节，过滤与任务无关的噪声表征。在具有挑战性的Meta-Dataset上，DETA持续提升了应用于多种预训练模型的广泛基线方法的性能。值得注意的是，通过解决Meta-Dataset中被忽视的图像噪声问题，DETA取得了新的最优结果。代码已发布于https://github.com/JimZAI/DETA。