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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 26912700 of 9051 papers

TitleStatusHype
Multi-Scale Implicit Transformer with Re-parameterize for Arbitrary-Scale Super-Resolution0
Attacking Transformers with Feature Diversity Adversarial Perturbation0
Speeding up 6-DoF Grasp Sampling with Quality-Diversity0
BSDA: Bayesian Random Semantic Data Augmentation for Medical Image ClassificationCode0
Can Generative Models Improve Self-Supervised Representation Learning?Code0
Augmentations vs Algorithms: What Works in Self-Supervised Learning0
CLIP-Gaze: Towards General Gaze Estimation via Visual-Linguistic Model0
PromptIQA: Boosting the Performance and Generalization for No-Reference Image Quality Assessment via PromptsCode1
SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target DetectionCode1
Face2Diffusion for Fast and Editable Face PersonalizationCode2
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