dRAE: Representation Autoencoder with Hyper-Spherical Codes

Key points

It clearly diagnoses the cause of VQ codebook collapse as a geometric mismatch between Euclidean distance and the geometry of high-dimensional representation spaces.
The best choice is a combination of angle-based, meaning cosine similarity, code assignment and Euclidean commitment loss, while using angles for everything hurts performance.
It keeps 100 percent codebook utilization even when the codebook scales to 131,072 entries, and it shows faster convergence and more stable training than VQ and IBQ.
On class-conditional ImageNet generation, it reaches gFID 4.45 with HSQ 65536, which is a large improvement over VQ, and it also performs well on text-to-image.
With only 12 million image-text pairs, it reaches GenEval 0.63 and DPG-Bench 80.58, which shows an efficient generation pipeline.

External research summaries. These are not HDATF publications or measured product results.