{"type":"ScholarlyArticle","title":"NanoAdapt: Mitigating Negative Transfer in Test Time Adaptation with Extremely Small Batch Sizes","authors":[{"name":"Shiji Zhao","slug":"shiji-zhao","given":"Shiji","family":"Zhao","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Shao-Yuan Li","slug":"shao-yuan-li","given":"Shao-Yuan","family":"Li","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"},{"name":"Sheng-Jun Huang","slug":"sheng-jun-huang","given":"Sheng-Jun","family":"Huang","affiliation":"College of Computer Science and Technology , Nanjing University of Aeronautics and Astronautics MIIT Key Laboratory of Pattern Analysis and Machine Intelligence"}],"abstract":"Test Time Adaptation (TTA) has garnered significant attention in recent years, with the research focus on addressing distribution shifts during test time. As one fundamental component of many TTA methods, the Batch Normalization (BN) layer plays a crucial role in enabling the model adaptability. However, existing BN strategies can prove detrimental when the batch size is (extremely) small. In numerous real-world scenarios, limited hardware resources or just-in-time demand often necessitates adjusting models with very small batch sizes, making existing methods less practical. In this paper, we first showcase and thoroughly analyze the negative transfer phenomenon in previous TTA methods encountering extremely small batch sizes. Subsequently, we propose a novel batch size-agnostic method called NanoAdapt to effectively mitigate the negative transfer even with batch size 1. NanoAdapt is composed of three key components: a dynamic BN calibration strategy that leverages historical information and the Taylor series to refine the statistics estimations, an entropy-weighted gradient accumulation strategy that uses the entropy of each sample's label prediction to weigh and accumulate the loss for backpropagation, and a novel proxy computation graph to capture the sample interactions. Extensive experiments are conducted to validate the superiority of NanoAdapt, showing its consistent efficacy in improving existing TTA methods.","doi":"10.24963/ijcai.2024/616","url":"https://ijcai.org/proceedings/2024/616","pdf":"https://ijcai.org/proceedings/2024/0616.pdf","markdown":"https://ijcai.org/proceedings/2024/616.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/616","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":616,"pages":{"first":5572,"last":5580},"keywords":["Computer Vision: CV: Transfer, low-shot, semi- and un- supervised learning","Machine Learning: ML: Multi-task and transfer learning"],"videos":[],"references":[{"position":1,"raw":"[Boudiaf et al., 2022] Malik Boudiaf, Romain Mueller, Is- mail Ben Ayed, and Luca Bertinetto. 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