用能量模型动态调整深度补全，提升模型在新环境下的适应能力。
ETA: Energy-based Test-time Adaptation for Depth Completion
- 通过对抗扰动构建能量模型，判断预测深度是否来自源数据分布。
- 在室内外6个数据集上，平均提升10.23%（室内）和6.94%（室外）。
- 无需目标数据先验，适合部署后实时优化深度补全模型。
我们提出一种预训练深度补全模型的测试时自适应方法。深度补全模型在源数据上训练后，迁移到新环境（目标数据）时常因协变量偏移产生错误预测。核心在于量化深度预测属于源数据分布的可能性。由于部署前无法获取目标数据，我们不假设目标分布，而是利用对抗扰动探索数据空间，训练一个能量模型对深度预测局部区域进行分布归属评分。在测试时更新预训练模型参数以最小化能量，有效将测试预测对齐源分布。该方法称为“基于能量的测试时自适应”（ETA）。我们在三个室内和三个室外数据集上评估，相较于之前最优方法，室外平均提升6.94%，室内平均提升10.23%。项目页：https://fuzzythecat.github.io/eta。
原文摘要 · Abstract (English)
We propose a method for test-time adaptation of pretrained depth completion models. Depth completion models, trained on some ``source'' data, often predict erroneous outputs when transferred to ``target'' data captured in novel environmental conditions due to a covariate shift. The crux of our method lies in quantifying the likelihood of depth predictions belonging to the source data distribution. The challenge is in the lack of access to out-of-distribution (target) data prior to deployment. Hence, rather than making assumptions regarding the target distribution, we utilize adversarial perturbations as a mechanism to explore the data space. This enables us to train an energy model that scores local regions of depth predictions as in- or out-of-distribution. We update the parameters of pretrained depth completion models at test time to minimize energy, effectively aligning test-time predictions to those of the source distribution. We call our method ``Energy-based Test-time Adaptation'', or ETA for short. We evaluate our method across three indoor and three outdoor datasets, where ETA improve over the previous state-of-the-art method by an average of 6.94% for outdoors and 10.23% for indoors. Project Page: https://fuzzythecat.github.io/eta.
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