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Test Time Adaptation (TTA) is a pivotal concept in machine learning, enablingmodels to perform well in real-world scenarios, where test data distributiondiffers from training. In this work, we propose a novel approach called pseudoSource guided Target Clustering (pSTarC) addressing the relatively unexploredarea of TTA under real-world domain shifts. This method draws inspiration fromtarget clustering techniques and exploits the source classifier for generatingpseudo-source samples. The test samples are strategically aligned with thesepseudo-source samples, facilitating their clustering and thereby enhancing TTAperformance. pSTarC operates solely within the fully test-time adaptationprotocol, removing the need for actual source data. Experimental validation ona variety of domain shift datasets, namely VisDA, Office-Home, DomainNet-126,CIFAR-100C verifies pSTarC's effectiveness. This method exhibits significantimprovements in prediction accuracy along with efficient computationalrequirements. Furthermore, we also demonstrate the universality of the pSTarCframework by showing its effectiveness for the continuous TTA framework. Thesource code for our method is available at https://manogna-s.github.io/pstarc

pSTarC: Pseudo Source Guided Target Clustering for Fully Test-Time Adaptation