This task has received a considerable amount of attention, particularly for classical ways of modeling distributions like structured prediction.

Graph few-shot learning is of great importance among various graph learning tasks. Under thefew-shot scenario, models areoftenrequired toconduct classification givenlimited labeled samples. Existing graph few-shot learning methods typically leverage Graph Neural Networks (GNNs) and perform classification across a series of meta-tasks. Nevertheless, these methods generally rely on the original graph (i.e., the graph that the meta-task is sampled from) to learn node representations.

Humans reason with concepts and metaconcepts: we recognizered and green from visual input; we also understand that theydescribe the same property of objects(i.e.,thecolor).

With the disentangled representations, we synthesize the counterfactual unbiased training samples to further decorrelate causal and bias variables.

Theconventional closed-set DAmethods generally assume that the source and target domains share the same label space. However, this assumption is often not realistic in practice.

Typically, DNNs suffer from drastic performance degradation of previously learned tasksafterlearning newknowledge, which isawell-documented phenomenon, knownascatastrophic forgetting [8,9,10].

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

Fairness and robustness are critical elements of Trustworthy AI that need to be addressedtogether.

Since deep neural networks (DNNs) became successful, domain adaptation methods also leveraged them for representation learning.

However,most existing studies concentrate onthetext-to-image synthesis [11-14], which generates images from text descriptions without the original image.