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 11311140 of 9051 papers

TitleStatusHype
Texture Reformer: Towards Fast and Universal Interactive Texture TransferCode1
Make It Move: Controllable Image-to-Video Generation with Text DescriptionsCode1
HIVE: Evaluating the Human Interpretability of Visual ExplanationsCode1
Revisiting Neuron Coverage for DNN Testing: A Layer-Wise and Distribution-Aware CriterionCode1
Controllable Video Captioning with an Exemplar SentenceCode1
Profiling Pareto Front With Multi-Objective Stein Variational Gradient DescentCode1
VoRTX: Volumetric 3D Reconstruction With Transformers for Voxelwise View Selection and FusionCode1
Generating Diverse 3D Reconstructions from a Single Occluded Face ImageCode1
Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum GamesCode1
Few-shot Image Generation with Mixup-based Distance LearningCode1
Show:102550
← PrevPage 114 of 906Next →

No leaderboard results yet.