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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

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Showing 1231–1240 of 9051 papers

TitleStatusHype
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Controllable Group Choreography using Contrastive DiffusionCode1
Feature Fusion from Head to Tail for Long-Tailed Visual RecognitionCode1
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using SamplesCode1
Beyond Trivial Counterfactual Explanations with Diverse Valuable ExplanationsCode1
An End-to-end Deep Reinforcement Learning Approach for the Long-term Short-term Planning on the Frenet SpaceCode1
An End-to-End Multi-Task Learning Model for Image-based Table RecognitionCode1
Few-shot Defect Image Generation based on Consistency ModelingCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
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