Playing with Vision Embeddings
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Beautiful illustrations
I find, 'Playing' is just the free and motivated version of 'exploration'.
One thought on your nicely illustrated "key observation [is] that neural networks tend to place features along directions": my guess is that the neural net was TOLD to behave that way by choosing e.g. Cosine Loss?
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For some reason, the uncanniness of the feature pictures are deeply unsettling for me. It just stirs intense unease. A bit amusing, to be honest.
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Very nice visualizations, thanks for that!
One thing I still struggle with in my head is how these vision embeddings can then be used to give LLMs eyes.
Because you somehow need a giant training set which describes images in natural language, no? Is that actually how it works, or is there some smart trick so you don't need to pay labellers a bunch of money to look at pictures and describe them.
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