Over the past decade, domain adaptation has become a widely studied branch of transfer learning which aims to improve performance on target domains by leveraging knowledge from the source domain. Conventional domain adaptation methods often assume access to both source and target domain data simultaneously, which may not be feasible in real-world scenarios due to privacy and confidentiality concerns. As a result, the research of Source-Free Domain Adaptation (SFDA) has drawn growing attention in recent years, which only utilizes the source-trained model and unlabeled target data to adapt to the target domain. Despite the rapid explosion of SFDA work, there has been no timely and comprehensive survey in the field. To fill this gap, we provide a comprehensive survey of recent advances in SFDA and organize them into a unified categorization scheme based on the framework of transfer learning. Instead of presenting each approach independently, we modularize several components of each method to more clearly illustrate their relationships and mechanisms in light of the composite properties of each method. Furthermore, we compare the results of more than 30 representative SFDA methods on three popular classification benchmarks, namely Office-31, Office-home, and VisDA, to explore the effectiveness of various technical routes and the combination effects among them. Additionally, we briefly introduce the applications of SFDA and related fields. Drawing on our analysis of the challenges confronting SFDA, we offer some insights into future research directions and potential settings.

Conventional machine learning algorithms suffer the problem that the model trained on existing data fails to generalize well to the data sampled from other distributions. To tackle this issue, unsupervised domain adaptation (UDA) transfers the knowledge learned from a well-labeled source domain to a different but related target domain where labeled data is unavailable. In this paper, we consider a more practical yet challenging UDA setting where either the source domain data or the target domain data are unknown. Technically, we investigate UDA from a novel view --- adversarial attack --- and tackle the divergence-agnostic adaptive learning problem in a unified framework. Specifically, we first report the motivation of our approach by investigating the inherent relationship between UDA and adversarial attacks. Then we elaborately design adversarial examples to attack the training model and harness these adversarial examples. We argue that the generalization ability of the model would be significantly improved if it can defend against our attack, so as to improve the performance on the target domain. Theoretically, we analyze the generalization bound for our method based on domain adaptation theories. Extensive experimental results verify that our method is able to achieve a favorable performance compared with previous ones.

The remaining useful lifetime (RUL) and state of charge (SoC) of rechargeable lithium-ion batteries (LIBs) are two integral parts to ensure LIBs working reliably and safely for transportation electrification systems. The two together reflect the state of a battery in use. However, existing capacity estimation approaches focus on separately modeling one of them, and no one has proposed a unified estimation model that is applicable to both RUL and SoC estimation yet. In this article, we propose a unified deep learning method that can be implemented for both RUL and SoC estimation. The proposed method leverages long short-term memory recurrent neural networks to achieve state-of-the-art accurate capacity estimation for LIBs under complex operating conditions. Notably, the unified method can perform not only one-step-ahead prediction but also multistep-ahead estimation with high accuracy, achieving RUL estimation error within ten cycles and SoC estimation error within 0.13%. Experimental data collected from battery testing systems with simulated complex operating conditions are used to train the method. A series of comparative experiments are conducted to compare our method with other existing methods. The experimental results show that our method can increase estimation accuracy and robustness for LIBs estimation problems via capturing the long-term dependencies among battery degradation data.

Data scarcity is a perpetual challenge of recommendation systems, and researchers have proposed a variety of cross-domain recommendation methods to alleviate the problem of data scarcity in target domains. However, in many real-world cross-domain recommendation systems, the source domain and the target domain are sampled from different data distributions, which obstructs the cross-domain knowledge transfer. In this article, we propose to specifically align the data distributions between the source domain and the target domain to alleviate imbalanced sample distribution and thus challenge the data scarcity issue in the target domain. Technically, our proposed approach builds a dual adversarial adaptation (DAA) framework to adversarially train the target model together with a pre-trained source model. Two domain discriminators play the two-player minmax game with the target model and guide the target model to learn reliable domain-invariant features that can be transferred across domains. At the same time, the target model is calibrated to learn domain-specific information of the target domain. In addition, we formulate our approach as a plug-and-play module to boost existing recommendation systems. We apply the proposed method to address the issues of insufficient data and imbalanced sample distribution in real-world Click-through Rate/Conversion Rate predictions on two large-scale industrial datasets. We evaluate the proposed method in scenarios with and without overlapping users/items, and extensive experiments verify that the proposed method is able to significantly improve the prediction performance on the target domain. For instance, our method can boost PLE with a performance improvement of 15.4% in terms of Area Under Curve compared with single-domain PLE on our private game dataset. In addition, our method is able to surpass single-domain MMoE by 6.85% on the public datasets. Code: https://github.com/TL-UESTC/DAA .

Accurate estimation of customer lifetime value (LTV), which reflects the potential consumption of a user over a period of time, is crucial for the revenue management of online advertising platforms. However, predicting LTV in real-world applications is not an easy task since the user consumption data is usually insufficient within a specific domain. To tackle this problem, we propose a novel cross-domain adaptative framework (CDAF) to leverage consumption data from different domains. The proposed method is able to simultaneously mitigate the data scarce problem and the distribution gap problem caused by data from different domains. To be specific, our method firstly learns a LTV prediction model from a different but related platform with sufficient data provision. Subsequently, we exploit domain-invariant information to mitigate data scarce problem by minimizing the Wasserstein discrepancy between the encoded user representations of two domains. In addition, we design a dual-predictor schema which not only enhances domain-invariant information in the semantic space but also preserves domain-specific information for accurate target prediction. The proposed framework is evaluated on five datasets collected from real historical data on the advertising platform of Tencent Games. Experimental results verify that the proposed framework is able to significantly improve the LTV prediction performance on this platform. For instance, our method can boost DCNv2 with the improvement of 13.7% in terms of AUC on dataset G2. Code: https://github.com/TL-UESTC/CDAF.

Face anti-spoofing (FAS) plays a crucial role in securing face recognition systems against presentation attacks. However, existing FAS methods often struggle to generalize to unseen attacks and domains. Existing generalizable FAS studies generally leverage domain generalization (DG) techniques for exploiting intermediate features that support generalization while neglecting the task-specific nature of FAS. In this paper, we argue that the FAS task is an imbalanced classification problem, which renders it unsuitable to be handled by a standard discriminative classifier. In contrast, we propose a novel approach for FAS by modeling the problem from a generative perspective using an energy-based model (EBM). The EBM captures the distribution of genuine faces and detects spoofing attempts as deviations from this distribution. We train the EBM using a discriminative objective and an energy regularization term to shape the learned distribution and improve generalization. To enhance the robustness to unseen domains, we introduce an energy-based domain augmentation technique that explores the latent space around the source distribution guided by the EBM. We further leverage a meta-learning framework and a gradient-based variant to leverage the augmented data for domain generalization. For practicability, we consider a practical setting where samples are holistically collected under different environments without distinct domain labels, and show that our method can naturally harness this challenging setting by training with cluster labels. Extensive experiments on four FAS datasets demonstrate the superiority of our method in both intra- and cross-dataset settings, outperforming state-of-the-art approaches.