Hugging Face's logo
AbstractPhila's picture
Open to Collab
18 3 22

AbstractPhila PRO

AbstractPhil
Winnougan's profile picture bendahou's profile picture FiditeNemini's profile picture
·
https://civitai.com/user/AbstractPhila

AI & ML interests

datasets, research papers, experimentation, vision, classification, text encoders, tokenization, llms, diffusion, distillation, and more.

Recent Activity

posted an update about 5 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.
liked a dataset about 6 hours ago
benjamin-paine/imagenet-1k-128x128
updated a collection about 6 hours ago
GeoLIP
View all activity

Organizations

DeepGHS's profile picture Blog-explorers's profile picture BangumiBase's profile picture Abstract Powered Research's profile picture