Energy Pocket is a compact class-conditional energy-based image model trained
with persistent contrastive divergence. Positive digit examples are assigned low
energy while replay-buffer negatives are refined with Langevin dynamics and
pushed upward. A discriminative energy loss anchors the ten class landscapes.
The evaluation measures frozen-judge fidelity, within-class variance, quantized
uniqueness, nearest-training-image distance, exact-copy rate, and the energy… See the full description on the dataset page:
https://huggingface.co/datasets/ARotting/energy-pocket-langevin-samples.
