From what I understand about these models, having studied and applied machine learning but not deep learning, they all build abstractions (embeddings) akin to e

From what I understand about these models, having studied and applied machine learning but not deep learning, they all build abstractions (embeddings) akin to eigenfaces[1][2] where many samples of a feature are turned into an abstract feature. These abstract features are then manipulated by the model. Sure, just like with Fourier transform-based compression, anything can be reconstructed from primitive features, it is called overfitting and the challenge of training these models is building a learning process that achieves generalization rather than overfitting. It is not true that 'it is not clear that there is abstract learning', because without it, generalization is impossible, and without generalization the model wouldn't pass even the most trivial benchmarks. [1]: https://media.geeksforgeeks.org/wp-content/uploads/20200317134836/train_faces.png https://media.geeksforgeeks.org/wp-content/uploads/202003171... [2]: https://www.geeksforgeeks.org/ml-face-recognition-using-eigenfaces-pca-algorithm/ https://www.geeksforgeeks.org/ml-face-recognition-using-eige...