Machine learning systems generally assume that the training and testing distributions are
the same. To this end, a key requirement is to develop models that can generalize to unseen …

Causal Machine Learning (CausalML) is an umbrella term for machine learning methods
that formalize the data-generation process as a structural causal model (SCM). This …

Abstract Three-dimensional (3D) cross-modality cardiac image segmentation is critical for
cardiac disease diagnosis and treatment. However, it confronts the challenge of modality …

Machine learning systems are known to be sensitive to spurious correlations between non-
essential features of the inputs (eg, background, texture, and secondary objects) and the …

Due to spurious correlations, machine learning systems often fail to generalize to
environments whose distributions differ from the ones used at training time. Prior work …

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 …

Deep learning has achieved tremendous success with independent and identically
distributed (iid) data. However, the performance of neural networks often degenerates …

Deep learning relies on learning from extensive data to generate prediction results. This
approach may inadvertently capture spurious correlations within the data, leading to models …

Albeit the notable performance on in-domain test points, it is non-trivial for deep neural
networks to attain satisfactory accuracy when deploying in the open world, where novel …

In recent years, remarkable achievements have been made in artificial intelligence tasks
and applications based on deep neural networks (DNNs), especially in the fields of vision …