mrtransisteur
mrtransisteur t1_j7xt1e5 wrote
Reply to [P] Creating an embedding from a CNN by zanzagaes2
You want to model:
p(cluster =c | img)
p(c1 == c2 | dist(c1, c2) = d, img1 in c1, img2 in c2)
You could try a couple things:
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Frechet Inception Distance but instead of Inception model you use the medical CNN activations
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distance metric learning
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hdbscan/umap/etc for clustering
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persistent homology based topological data analysis methods for finding clusters
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masked autoencoders for good feature extraction
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JEPA style architecture
mrtransisteur t1_iuqcuoz wrote
Reply to [R] Is there any work being done on reduction of training weight vector size but not reducing computational overhead (eg pruning)? by Moose_a_Lini
Yep- see Weight Fixing Networks https://arxiv.org/abs/2210.13554
mrtransisteur t1_isvjfeu wrote
Reply to comment by zergling103 in [D] Could diffusion models be succesfully trained to reverse distortions other than noise? by zergling103
Sounds like you've got a research paper topic brewing!
mrtransisteur t1_jasibpc wrote
Reply to [D] Is there an ML project out there that recommends movies based on more than the usual features? by of_a_varsity_athlete
I’ve been using ChatGPT for exactly this, vibe based movie recs. One trick is to encourage it to suggest older classics, lesser-known films, lower budget films, or foreign films. Just tell it when you’ve already seen the movie or if there’s some other misalignment between the suggestions and what you want to see