Since distribution shifts are likely to occur after a model's deployment and can drastically decrease the model's performance, online test-time adaptation (TTA) continues to update the model during test-time, leveraging the current test data. In real-world scenarios, test data streams are not always independent and identically distributed (i.i.d.). Instead, they are frequently temporally correlated, making them non-i.i.d. Many existing methods struggle to cope with this scenario. In response, we propose a diversity-aware and category-balanced buffer that can simulate an i.i.d. data stream, even in non-i.i.d. scenarios. Combined with a diversity and entropy-weighted entropy loss, we show that a stable adaptation is possible on a wide range of corruptions and natural domain shifts, based on ImageNet. We achieve state-of-the-art results on most considered benchmarks.

翻译：由于模型部署后可能出现分布偏移，且这会显著降低模型性能，在线测试时适应（TTA）利用当前测试数据持续更新模型。在实际场景中，测试数据流通常不满足独立同分布（i.i.d.）假设，反而常呈现时间相关性，形成非独立同分布（non-i.i.d.）数据。现有方法大多难以应对此类场景。为此，我们提出一种具有多样性感知与类别平衡的缓冲区，能够在非独立同分布条件下模拟独立同分布数据流。结合多样性加权熵与加权熵损失，我们证明在基于ImageNet的广泛 corruption 和自然域偏移上均可实现稳定适应。在大多数基准测试中，我们取得了最先进的结果。