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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 49614970 of 9051 papers

TitleStatusHype
FaceFormer: Scale-aware Blind Face Restoration with Transformers0
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Difficulty-Aware Simulator for Open Set RecognitionCode1
Large Scale Radio Frequency Signal ClassificationCode2
DH-AUG: DH Forward Kinematics Model Driven Augmentation for 3D Human Pose EstimationCode1
FakeCLR: Exploring Contrastive Learning for Solving Latent Discontinuity in Data-Efficient GANsCode1
Towards Diverse and Faithful One-shot Adaption of Generative Adversarial NetworksCode1
Unsupervised Medical Image Translation with Adversarial Diffusion ModelsCode2
GANzilla: User-Driven Direction Discovery in Generative Adversarial Networks0
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
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