Unsupervised domain adaptation has attracted appealing academic attentions by transferring knowledge from labeled source domain to unlabeled target domain.

In this paper, we introduce a new type of model backdoor: the privacy backdoor attack .

Deidentification algorithms are vulnerable to the same bias and privacy issues that impact other data analytics and machine learning applications, and it can even amplify those issues by contaminating downstream applications.

In this paper, we present MixV al, an innovative model selection method that operates solely with unlabeled target data during inference.

Conditional score-based diffusion model (SBDM) is for conditional generation of target data with paired data as condition, and has achieved great success in image translation. However, it requires the paired data as condition, and there would be insufficient paired data provided in real-world applications.

Neural Information Processing Systems http://nips.cc/

Westudy linear regression under covariate shift, where themarginal distribution over the input covariates differs in the source and the target domains, while the conditional distribution of the output given the input covariates is similar across thetwodomains.

As a dominant paradigm, fine-tuning a pre-trained model on the target data is widely used in many deep learning applications, especially for small data sets.