Crossdataset

Crossdataset refers to approaches and practices that involve multiple datasets with the goal of training, evaluating, or transferring knowledge across data sources. In machine learning and data science, crossdataset describes a setting where data originate from more than one dataset, often with different distributions, labeling schemes, or acquisition conditions. Crossdataset learning encompasses training models on one or more datasets and applying or testing them on another, while cross-dataset evaluation focuses on assessing generalization across datasets.

Applications and motivation: The approach addresses dataset shift and domain shift, promoting robustness to changes in data source. It is widely used in computer vision, natural language processing, and speech recognition where data are collected from diverse sensors, regions, or annotation pipelines.

Techniques and methods: Domain adaptation aims to align distributions between source and target datasets, via supervised or unsupervised signals. Domain generalization seeks models that perform well on unseen datasets without access to target data during training. Methods include feature alignment losses (e.g., MMD, CORAL), adversarial training with a domain discriminator, invariant representation learning, and meta-learning across datasets. Data preprocessing and normalization strategies can reduce inter-dataset variability. Multi-dataset training, curriculum learning, and transfer learning are common enablers.

Challenges: Heterogeneous label spaces, annotation differences, class imbalance, and varying data quality complicate crossdataset learning. Privacy, licensing, and data access constraints may restrict dataset usage. Evaluation can be nontrivial due to differences in metrics and class imbalances.

See also: Domain adaptation, transfer learning, domain generalization, covariate shift, concept drift, multi-task learning.