论文标题
通过主成分分析测试时间适应
Test-Time Adaptation with Principal Component Analysis
论文作者
论文摘要
当测试数据与培训数据不同时，机器学习模型很容易失败，这种情况通常在称为分配转移的真实应用程序中遇到。尽管仍然有效，但培训时间知识的效率较低，需要进行测试时间的适应才能保持高性能。以下方法假定批处理层并使用其统计数据进行适应，我们提出了使用主成分分析（TTAWPCA）的测试时间适应，该测试时间推测拟合的PCA并在测试时间适应了基于PCA的奇异值的频谱过滤器，以使PCA的奇异值鲁棒性腐败。 TTAWPCA结合了三个组件：使用主成分分析（PCA）分解给定层的输出，并通过其单数值的惩罚过滤，并用PCA逆变换重建。与当前方法相比，这种通用增强功能增加的参数少。在CIFAR-10-C和CIFAR-100-C上进行的实验证明了使用2000参数的唯一滤波器的有效性和限制。
Machine Learning models are prone to fail when test data are different from training data, a situation often encountered in real applications known as distribution shift. While still valid, the training-time knowledge becomes less effective, requiring a test-time adaptation to maintain high performance. Following approaches that assume batch-norm layer and use their statistics for adaptation, we propose a Test-Time Adaptation with Principal Component Analysis (TTAwPCA), which presumes a fitted PCA and adapts at test time a spectral filter based on the singular values of the PCA for robustness to corruptions. TTAwPCA combines three components: the output of a given layer is decomposed using a Principal Component Analysis (PCA), filtered by a penalization of its singular values, and reconstructed with the PCA inverse transform. This generic enhancement adds fewer parameters than current methods. Experiments on CIFAR-10-C and CIFAR- 100-C demonstrate the effectiveness and limits of our method using a unique filter of 2000 parameters.
