5 ms·

Cosine similarity is a fixed way of comparing two vectors, so we can think of it as making a difference: A-B = d If d is close to 0, we say that both embedding

by kuu 4y ago

Cosine similarity is a fixed way of comparing two vectors, so we can think of it as making a difference: A-B = d If d is close to 0, we say that both embeddings are similar. If d is close to 1, we say that both embeddings are different. Imagine we have the following data: - Image A and its description A - Image B and its description B We would generate the following dataset: - Image A & Description A. Expected label: 0 - Image B & Description B. Expected label: 0 - Image A & Description B. Expected label: 1 - Image B & Description A. Expected label: 1 The mixture of Image Y with Description Z with Y!=Z is what we call "negative sampling" If the model predicts 1 but the expected value was 0 (or the other way around), it's a miss, and therefore the model is "penalized" and has to adjust the weights; if the prediction matches the expectation, it's a success, the model is not modified. I hope this clears it

spywaregorilla 4y ago
I'd be curious to see an example gallery of image generation of the same vector scaled to different magnitudes. That is, 100% cosine similarity, but still hitting different points of the embedding space. The outcome vectors aren't normalized right? So there could be a hefty amount of difference in this space? Maybe not on concept, but perhaps on image quality?
- kuu 4y ago
  > The outcome vectors aren't normalized right? I'm not sure about it, maybe they are, it wouldn't be strange > So there could be a hefty amount of difference in this space? Maybe not on concept, but perhaps on image quality? Sure, each text could have more than one image matching the same representation (cosine wise), but maybe the changes wouldn't look much as "concepts" in the image but other features (sharpness, light, noise, actual pixel values, etc) It would be curious to check, definitely
uptown 4y ago
Thanks very much! That helped me understand the concept better.

by kuu 4y ago

spywaregorilla 4y ago

I'd be curious to see an example gallery of image generation of the same vector scaled to different magnitudes. That is, 100% cosine similarity, but still hitting different points of the embedding space. The outcome vectors aren't normalized right? So there could be a hefty amount of difference in this space? Maybe not on concept, but perhaps on image quality?

kuu 4y ago
> The outcome vectors aren't normalized right? I'm not sure about it, maybe they are, it wouldn't be strange > So there could be a hefty amount of difference in this space? Maybe not on concept, but perhaps on image quality? Sure, each text could have more than one image matching the same representation (cosine wise), but maybe the changes wouldn't look much as "concepts" in the image but other features (sharpness, light, noise, actual pixel values, etc) It would be curious to check, definitely

uptown 4y ago

Thanks very much! That helped me understand the concept better.