junetwentyfirst2020

junetwentyfirst2020 t1_j34427r wrote

What you’re looking for is embeddings. Take an auto encoder and produce an embedding (latent code) for every image in your database. When you need to query for an image, produce an embedding for that image and use a nearest neighbors algorithm to find the most similar images.

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junetwentyfirst2020 t1_j2xxhii wrote

That’s not an easy question to answer because the 90% that are correct may be super easy to fit, and those 10% errors may just be unfittable and will just keep the loss high without impacting the model. On the other hand, since models tend to be very over-parameterized that 10% could very well be “fit” and have an outsized impact on the model. It could also be the case that the model ends up with 10% variance on its accuracy.

I’ve never seen a definitive theoretical answer since deep learning models are over parameterized and have seen models replicate the error in the training data, especially when it came to keypoint prediction. When I assessed the error in the training data, I showed the team that the model has the same degree of error. I was arguing for cleaner training data. I got told no and to come up with a magic solution to fix the problem. I quit 🤣

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