Interestingly, although the diffusion model shows great potential in 3D generation tasks, e.g.

Since our manually collected test sets are rather small, we decided to avoid tuning hyperparameters on them as this would require holding out a non-trivial number of data points.

The major differences are as follows: 1) we use dropout and BN with weight normalization (WN) as a regularizer instead of the existing techniques such as spectral normalization (SN) and gradient penalties (GP). The BN is proven to function as a regularizer imposing the Lipschitz constraint [9], which has been achieved by SN and GP [3, 7]. Plus, the dropout and WN have been successfully adopted in the classifier-based model[12]. The learning rates of the discriminator and the generator are set according to two-timescale learning rate (TTUR) [4], which is adopted in Proj. SNGAN sets the learning rates ofthe discriminator and the generator as0.0004 and 0.0001, respectively,andtheyarefixedoverthecourse ofthetraining.

Wealso extendtheproposed method forconditional settings withclasslabelsbycreating a hypersphere for each category and performing class-wise discrimination and update.

'Scale the model, scale the data, scale the compute' is the reigning sentiment in the world of generative AI today.

However,twokeydrawbacks of RPS-l2 are thatthey(i)leadtodisagreement between theoriginally trained networkandthe RPS-l2 regularized network modification and (ii) often yield a static ranking of training data fortest points inthesame class, independent ofthetest point being classified. Inspired by the RPS-l2 approach, we propose an alternative method based on a local Jacobian Taylor expansion (LJE).

Understanding treatment effect heterogeneity is vital for scientific and policy research.