I have been working on an improved optimization algorithm for my Siamese network. I have done this using a dot matrix. What this means is that I have taken the pairwise dot product between the embeddings of two images. These two images are either the corrupted and non-corrupted versions of the same image, or two different images. The two different images will be referred to as Set A, and the two same images will be referred to as Set B.

Set A, the cosine similarity matrix(same image) in the image below, shows very promising results, matching up with what is hoped. A majority of the image is random noise, representing a low correlation between different parts of the embedding vector. This is intended because I want to maximize entropy, meaning it can store the most information in each part of the embedding vector. The main diagonal of Set A(the one going from the top left to the bottom right) shows a very high value. This means that the embedding vectors of the corrupted and non-corrupted versions of an image are very similar. Set B is very noisy, meaning that the embedding vectors of unrelated images are very dissimilar, exactly what I want.

These results are very promising, allowing me to work on interesting uses of this model, which will be the focus of my next few devlogs.