Published on June 1, 2023
Transfer Learning
Overfitting on smaller datasets: Yes, if you have trained a model on a large dataset like ImageNet, it has learned to extract useful features from the images, which will likely be beneficial for your smaller dataset. However, if you fine-tune (i.e., continue training) the model on a very small dataset, there's a risk that the model might overfit to this small dataset. Overfitting happens when a model learns to perform very well on the training data, but poorly on unseen data. It essentially learns the "noise" in the training data. So, while the model has learned general features from ImageNet, fine-tuning it on a small dataset might cause it to learn features that are too specific to your training data and perform poorly on new, unseen data. This is why when fine-tuning on small datasets, it's usually recommended to only fine-tune the last few layers of the model, rather than the whole model.
Transfer learning across related tasks: Transfer learning works best when the source and target tasks are related. This is because the features that a model learns for one task are more likely to be useful for a similar task. However, how similar the tasks need to be can vary. In your example, a model trained for car detection would likely be useful for truck detection, because both tasks involve recognizing similar features (wheels, windows, vehicle body, etc.). However, a model trained on car detection might not perform as well if you tried to use it for a very different task, like diagnosing diseases from medical images. It's hard to give a strict rule on how related the tasks need to be, because it can depend on many factors. However, in general, the more similar the tasks, the better transfer learning is likely to work.