Abstract Invariant Risk Minimization (IRM) is an emerging invariant feature extracting
technique to help generalization with distributional shift. However, we find that there exists a …

Abstract Deep Neural Networks have exhibited considerable success in various visual tasks.
However, when applied to unseen test datasets, state-of-the-art models often suffer …

Distributionally robust optimization (DRO) and invariant risk minimization (IRM) are two
popular methods proposed to improve out-of-distribution (OOD) generalization performance …

Domain generalization (DG) aims at learning generalizable models under distribution shifts
to avoid redundantly overfitting massive training data. Previous works with complex loss …

Abstract Design automation is a core technology in industrial design software and an
important branch of knowledge-worker automation. For example, electronic design …

Graph neural architecture search has shown great potentials for automatically designing
graph neural network (GNN) architectures for graph classification tasks. However, when …

The proliferation of pretrained models, as a result of advancements in pretraining
techniques, has led to the emergence of a vast zoo of publicly available models. Effectively …

Recent advances on large-scale pre-training have shown great potentials of leveraging a
large set of Pre-Trained Models (PTMs) for improving Out-of-Distribution (OoD) …

As a recent noticeable topic, domain generalization (DG) aims to first learn a generic model
on multiple source domains and then directly generalize to an arbitrary unseen target …

Contrastive language-image pre-trained (CLIP) models have zero-shot ability of classifying
an image belonging to “[CLASS]” by using similarity between the image and the prompt …