AutoML does not support transfer learning, as it relies on training models from scratch for each dataset.

AutoML supports transfer learning by fine-tuning pre-trained models on the target dataset, but it does not provide any specific techniques for this process.

AutoML supports transfer learning by using techniques such as feature extraction, where the pre-trained model is used to extract relevant features from the data, and fine-tuning, where the pre-trained model is adapted to the target dataset by training the final layers on the new data.

AutoML supports transfer learning by automatically selecting a pre-trained model that is similar to the target task and using it as a starting point for training the new model.