Domänförskjutning

Domänförskjutning, or domain shift, refers to a phenomenon in machine learning and artificial intelligence where the distribution of data encountered during testing or deployment differs from the distribution of data used during training. This discrepancy can significantly degrade the performance of a model, as it was not trained to handle these new data characteristics.

There are several types of domain shift. Covariate shift occurs when the input features' distribution changes, but the conditional distribution of the target variable given the features remains the same. For example, a model trained on images of cats in good lighting might perform poorly on images taken in low light. Concept drift happens when the relationship between the input features and the target variable changes over time. This is common in applications like spam detection, where spammers constantly adapt their techniques.

Recognizing and addressing domain shift is crucial for building robust and reliable AI systems. Several techniques exist to mitigate its effects, including domain adaptation, where models are trained or fine-tuned on a smaller amount of labeled data from the target domain. Domain generalization aims to train models that can perform well across a wide range of unseen domains without requiring any target domain data during training. Feature alignment methods seek to transform features from different domains into a common space, making them more comparable. Monitoring model performance in production is also vital to detect and respond to domain shift as it occurs.