Dynamic Adaptation on Non-Stationary Visual Domains

TL;DR

This paper presents a framework for continuous, sequential domain adaptation in non-stationary, streaming data environments, improving model performance over time without additional annotations.

Contribution

It introduces a novel framework for sequential domain adaptation on streaming data, extending associative domain adaptation to handle unequal class distributions and applying it to classification and segmentation.

Findings

Improved classifier accuracy over multiple streaming batches.

Effective adaptation with unequal class distributions.

Competitive results in semantic segmentation.

Abstract

Domain adaptation aims to learn models on a supervised source domain that perform well on an unsupervised target. Prior work has examined domain adaptation in the context of stationary domain shifts, i.e. static data sets. However, with large-scale or dynamic data sources, data from a defined domain is not usually available all at once. For instance, in a streaming data scenario, dataset statistics effectively become a function of time. We introduce a framework for adaptation over non-stationary distribution shifts applicable to large-scale and streaming data scenarios. The model is adapted sequentially over incoming unsupervised streaming data batches. This enables improvements over several batches without the need for any additionally annotated data. To demonstrate the effectiveness of our proposed framework, we modify associative domain adaptation to work well on source and target data batches with unequal class distributions. We apply our method to several adaptation benchmark datasets for classification and show improved classifier accuracy not only for the currently adapted batch, but also when applied on future stream batches. Furthermore, we show the applicability of our associative learning modifications to semantic segmentation, where we achieve competitive results.