AbstractPhil/ryan-spearman-prepared-features
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AbstractPhil
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repliedto their post about 11 hours ago
Say hello to surge resonance training. From random init, 1 epoch trained the 128x128 imagenet SVAE with test reconstruction over 99% accurate by epoch 1 to 99.9% accurate by epoch 5.
https://huggingface.co/AbstractPhil/geolip-SVAE
Epoch 1 test recon error 0.0064
Epoch 2 test recon error 0.0022
Epoch 8 is now 0.000294
Epoch 12 is now 0.000206
Epoch 14 is now 0.000190
Epoch 18 is now 0.000187
Epoch 24 is now 0.000117
Epoch 30 landmark 0.000099
There are NO EXPERTS HERE. This is pure self learning. The model learns the entire behavioral set within 1 epoch to reconstruct imagenet's test set to a useful state. By epoch 12 a recon of 0.000202 recall is now measured. This means, 99.99% accuracy at RECONSTRUCTING the test set through the bottleneck, while simultaneously leaving a trail of centerwise extraction as rich or richer.
ONE epoch. Just one.
Took about 10 minutes to train an already converged epoch, and I set it up for 200 epochs. This model will not need 200 epochs. I'd be surprised if it needs 3.
What you're looking at here, is the emergence of surge resonance. The power of a single epoch when the geometric CV alignment hits the tuning fork of absolute resonant perfection and counterpointed with the concerto's dissonant harmonic response.
I give you, surge resonance.
The metrics will be ready by morning and I'll begin building utilities to figure out what went right and what went wrong.
This model is rewarded when it exists within the geometric spectrum while simultaneously dual punished when leaving. There is no benefit to stray, and the benefit to exist within prevents the model from leaving the validated CV band.
This allows the model to exist perfectly within the tuning fork resonance structure.
The model CONTINUES to refine, even when the CV drift has begun to drift away from home. The model has left home and is now seeking new proximity.
Upcoming training will be the 256x256, 512x512, 1024x1024, and larger if the model holds. Each will be named. updated a model about 16 hours ago
AbstractPhil/geolip-SVAE updated a model about 17 hours ago
AbstractPhil/geolip-omega-diffusion-128Organizations
