SOTAVerified

Disentanglement

This is an approach to solve a diverse set of tasks in a data efficient manner by disentangling (or isolating ) the underlying structure of the main problem into disjoint parts of its representations. This disentanglement can be done by focussing on the "transformation" properties of the world(main problem)

Papers

Showing 4150 of 1854 papers

TitleStatusHype
Benchmarking Uncertainty Disentanglement: Specialized Uncertainties for Specialized TasksCode2
Fine-Grained Face Swapping via Regional GAN InversionCode2
BlendFace: Re-designing Identity Encoders for Face-SwappingCode2
HiCo: Hierarchical Controllable Diffusion Model for Layout-to-image GenerationCode2
Interpreting the Latent Space of GANs for Semantic Face EditingCode2
A Hierarchical Representation Network for Accurate and Detailed Face Reconstruction from In-The-Wild ImagesCode2
MotionCLIP: Exposing Human Motion Generation to CLIP SpaceCode2
ColorPeel: Color Prompt Learning with Diffusion Models via Color and Shape DisentanglementCode2
CausalVAE: Structured Causal Disentanglement in Variational AutoencoderCode2
When StyleGAN Meets Stable Diffusion: a W+ Adapter for Personalized Image GenerationCode2
Show:102550
← PrevPage 5 of 186Next →

No leaderboard results yet.