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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 1301–1310 of 9051 papers

TitleStatusHype
Continual Learning for Image Segmentation with Dynamic QueryCode1
HIVE: Evaluating the Human Interpretability of Visual ExplanationsCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality DataCode1
How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative ModelsCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
How well can Text-to-Image Generative Models understand Ethical Natural Language Interventions?Code1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
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