Deep Domain Confusion: Maximizing for Domain Invariance

TLDR

Generic supervised deep CNNs reduce but do not eliminate dataset bias, and fine‑tuning for new domains often lacks sufficient data. The authors propose a CNN with an adaptation layer and domain‑confusion loss to learn semantically meaningful, domain‑invariant representations. They use a domain‑confusion metric to guide model selection, determining the adaptation layer’s size and optimal placement within the CNN. The adaptation method outperforms prior approaches on a standard visual domain adaptation benchmark.

Abstract

Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark. Fine-tuning deep models in a new domain can require a significant amount of data, which for many applications is simply not available. We propose a new CNN architecture which introduces an adaptation layer and an additional domain confusion loss, to learn a representation that is both semantically meaningful and domain invariant. We additionally show that a domain confusion metric can be used for model selection to determine the dimension of an adaptation layer and the best position for the layer in the CNN architecture. Our proposed adaptation method offers empirical performance which exceeds previously published results on a standard benchmark visual domain adaptation task.