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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 721730 of 9051 papers

TitleStatusHype
ConsistencyTTA: Accelerating Diffusion-Based Text-to-Audio Generation with Consistency DistillationCode1
AffordPose: A Large-scale Dataset of Hand-Object Interactions with Affordance-driven Hand PoseCode1
Masked Generative Modeling with Enhanced Sampling SchemeCode1
Nucleus-aware Self-supervised Pretraining Using Unpaired Image-to-image Translation for Histopathology ImagesCode1
Large-Vocabulary 3D Diffusion Model with TransformerCode1
UnifiedGesture: A Unified Gesture Synthesis Model for Multiple SkeletonsCode1
Few-Shot Medical Image Segmentation via a Region-enhanced Prototypical TransformerCode1
Score-PA: Score-based 3D Part AssemblyCode1
AnthroNet: Conditional Generation of Humans via AnthropometricsCode1
Everyone Deserves A Reward: Learning Customized Human PreferencesCode1
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