Domain Adaptation

Domain adaptation is a technique in machine learning where a model trained on one type of data (the source domain) is adjusted to perform well on a different but related type of data (the target domain). For example, a model trained to recognize objects in daytime photos can be adapted to work better with nighttime images. This process helps the model handle variations between different environments or data sources, improving its accuracy when applied in new contexts without needing to start training from scratch.