Learning domain-invariant representation is a dominant approach for domain generalization. However, previous methods based on domain invariance overlooked the underlying dependency of classes on domains, which is responsible for the trade-off between classification accuracy and the invariance. This study proposes a novel method {\em adversarial feature learning under accuracy constraint (AFLAC)}, which maximizes domain invariance within a range that does not interfere with accuracy. Empirical validations show that the performance of AFLAC is superior to that of baseline methods, supporting the importance of considering the dependency and the efficacy of the proposed method to overcome the problem. In supervised learning we typically assume that samples are obtained from the same distribution in training and testing; however, because this assumption does not hold in many practical situations it reduces the classification accuracy for the test data BID20 . One typical situation is domain generalization (DG) BID1 BID18 BID19 BID2 : we have labeled data from several source domains and collectively exploit them such that the trained system generalizes to other unseen, but somewhat similar, target domain(s). This paper considers DG under the situation where domain d and class y labels are statistically dependent owing to some common latent factor z FIG0 -(c)), which we referred to as domainclass dependency. For example, the WISDM Activity Prediction dataset (WISDM, BID10 ), where y and d correspond to activities and wearable device users, exhibits this dependency because (1) some activities (e.g., jogging) are strenuous to the extent that some unathletic subjects avoided them (data characteristics), or (2) other activities were added only after the study began and the initial subjects could not perform them (data-collection errors). The dependency is common in real-world datasets BID23 and a similar setting has been investigated in domain adaptation (DA) studies, but most prior DG studies overlooked the dependency.Most prior DG methods utilize invariant feature learning (IFL) (e.g., ). IFL attempts to learn feature representation h from input data x which is invariant to d. When source and target domains have some common structure (see, ), IFL prevents the classifier from overfitting to source domains FIG0 ). However, under the dependency, merely imposing the domain invariance can adversely affect the classification accuracy as pointed out by BID21 and illustrated in FIG0 . Although that trade-off occurs in source domains (because DG uses only source data during optimization), it can also negatively affect the classification performance for target domain(s). For example, if the target domain has characteristics similar (or same as an extreme case) to those of a certain source domain, giving priority to domain invariance obviously interferes with the DG performance ( FIG0 ).In this paper, considering that prioritizing domain invariance under the trade-off can negatively affect the DG performance, we propose a novel method adversarial feature learning under accuracy constraint (AFLAC), which maximizes domain invariance within a range that does not interfere with the classification accuracy FIG0 -(e)) on adversarial training. Specifically , AFLAC is intended to achieve accuracy-constrained domain invariance, which we define as the maximum H(d|h) (H denotes entropy) value under the condition H(y|x) = H(y|h) (h has as much y information as x). Empirical validations show that the performance of AFLAC is superior to that of baseline methods, supporting the importance of considering domain-class dependency and the efficacy of the proposed approach for overcoming the issue. In this paper, we proposed a novel method AFLAC, which maximizes domain invariance within a range that does not interfere with classification accuracy on adversarial training. Empirical validations show the superior DG performance of AFLAC to the baseline methods, supporting the importance of the domain-class dependency in domain generalization tasks and the efficacy of the proposed method for overcoming the issue.
Address the trade-off caused by the dependency of classes on domains by improving domain adversarial nets
{"src": "scitldr-AIC_train", "paper_id": "Hkxj_LpvvV"}