Domain Adaptation Vs Fine Tuning

Fine tuning a pre trained network is a type of transfer learning.

Domain adaptation vs fine tuning. Rest assured these are all related and try to solve similar problems. Transfer learning is commonly understood to be the problem of taking what you learned in problem a and applying it to problem b. Hence it is sometimes confusing to differentiate between transfer learning domain adaptation and multi task learning.

Domain adaptation is a field associated with machine learning and transfer learning this scenario arises when we aim at learning from a source data distribution a well performing model on a different but related target data distribution. As by using preinitialized weights instead of random ones you are in effect transfering knowledge from one domain to another. Webcam model 1 vs webcam model 2.

Transfer learning or domain adaptation is related to the difference in the distribution of the train and test set. Fine tuning off the shelf pre trained models. In this paper we tackle the new cross domain few shot learning benchmark proposed by the cvpr 2020 challenge.

As you will notice this list is currently mostly focused on domain adaptation da and domain to domain translation. During breast cancer progression the epithelial to mesenchymal transition has been associated with metastasis and endocrine therapy resistance. Day vs night synthetic data e g rasterization vs prof.

So it is something broader than fine tuning which means that we know a priori that the train and test come from different distribution and we are trying to tackle this problem with several techniques depending on the kind of difference instead of just trying to adjust some. It really depends on the context in which those terms are being used. One could argue domain adaption is the correct term here but almost all the literature i ve seen calls it transfer learning via fine tuning or something similar.

This is a more involved technique where we do not just replace the final layer. A list of awesome papers and cool resources on transfer learning domain adaptation and domain to domain translation in general. Inspired by the need to create.