EMTL: A GENERATIVE DOMAIN ADAPTATION APPROACH

Abstract

We propose an unsupervised domain adaptation approach based on generative models. We show that when the source probability density function can be learned, one-step Expectation-Maximization iteration plus an additional marginal density function constraint will produce a proper mediator probability density function to bridge the gap between the source and target domains. The breakthrough is based on modern generative models (autoregressive mixture density nets) that are competitive to discriminative models on moderate-dimensional classification problems. By decoupling the source density estimation from the adaption steps, we can design a domain adaptation approach where the source data is locked away after being processed only once, opening the door to transfer when data security or privacy concerns impede the use of traditional domain adaptation. We demonstrate that our approach can achieve state-of-the-art performance on synthetic and real data sets, without accessing the source data at the adaptation phase.

1. INTRODUCTION

In the classical supervised learning paradigm, we assume that the training and test data come from the same distribution. In practice, this assumption often does not hold. When the pipeline includes massive data labeling, models are routinely retrained after each data collecion campaign. However, data labeling costs often make retraining impractical. Without labeled data, it is still possible to train the model by using a training set which is relevant but not identically distributed to the test set. Due to the distribution shift between the training and test sets, the performance usually cannot be guaranteed. Domain adaptation (DA) is a machine learning subdomain that aims at learning a model from biased training data. It explores the relationship between source (labeled training data) and target (test data) domains to find the mapping function and fix the bias, so that the model learned on the source data can be applied in target domain. Usually some target data is needed during the training phase to calibrate the model. In unsupervised domain adaptation (UDA) only unlabeled target data is needed during training phase. UDA is an appealing learning paradigm since obtaining unlabeled data is usually easy in a lot of applications. UDA allows the model to be deployed in various target domains with different shifts using a single labeled source data set. Due to these appealing operational features, UDA has became a prominent research field with various approaches. Kouw & Loog (2019) and Zhuang et al. ( 2020) surveyed the latest progress on UDA and found that most of the approaches are based on discriminative models, either by reweighting the source instances to approximate the target distribution or learning a feature mapping function to reduce the statistical distance between the source and target domains. After calibrating, a discriminative model is trained on the adjusted source data and used in target domain. In this workflow, the adaptation algorithm usually have to access the source and target data simultaneously. However, accessing the source data during the adaptation phase is not possible when the source data is sensitive (for example because of security or privacy issues). In particular, in our application workflow an industrial company is selling devices to various service companies which cannot share their customer data with each other. The industrial company may contract with one of the service companies to access their data during an R&D phase, but this data will not be available when the industrial company sells the device (and the predictive model) to other service companies.

