SOTAVerified

Diversity

Diversity in data sampling is crucial across various use cases, including search, recommendation systems, and more. Ensuring diverse samples means capturing a wide range of variations and perspectives, which leads to more robust, unbiased, and comprehensive models. In search use cases, for instance, diversity helps avoid redundancy, ensuring that users are exposed to a broader set of relevant information rather than repeated similar results.

Papers

Showing 141150 of 9051 papers

TitleStatusHype
Diffusion Models Beat GANs on Image SynthesisCode2
Test-time Alignment of Diffusion Models without Reward Over-optimizationCode2
Diffusion Models for Molecules: A Survey of Methods and TasksCode2
DiffTF++: 3D-aware Diffusion Transformer for Large-Vocabulary 3D GenerationCode2
DifFlow3D: Toward Robust Uncertainty-Aware Scene Flow Estimation with Iterative Diffusion-Based RefinementCode2
DiffuSeq: Sequence to Sequence Text Generation with Diffusion ModelsCode2
Diff-BGM: A Diffusion Model for Video Background Music GenerationCode2
Diffusion Bridge Implicit ModelsCode2
DiffusionPen: Towards Controlling the Style of Handwritten Text GenerationCode2
3DGen: Triplane Latent Diffusion for Textured Mesh GenerationCode2
Show:102550
← PrevPage 15 of 906Next →

No leaderboard results yet.