FuseReg trains decoders and DiTs on random encoder-layer subsets, letting one decoder reconstruct from full, sparse, and single-layer fusions and lowering gFID with the generator unchanged
The work introduces FuseReg, which replaces heuristic layer selection in representation autoencoders (RAEs) with training over random subsets of encoder layers: on ImageNet-256 with a frozen DINOv3-L, a single FuseReg decoder reconstructs from full, sparse, and single-layer fusions without retraining and achieves higher PSNR than decoders specialized to fixed fusions, while decoder replacement alone (with an unchanged RAEv2 DiT-XL generator) reduces unguided gFID and joint regularization of decoder and generator further reduces unguided gFID on DiT-Base.