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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 11211130 of 9051 papers

TitleStatusHype
Network Inversion and Its Applications0
SVGDreamer++: Advancing Editability and Diversity in Text-Guided SVG GenerationCode3
Large-Scale Data-Free Knowledge Distillation for ImageNet via Multi-Resolution Data GenerationCode0
DiffSLT: Enhancing Diversity in Sign Language Translation via Diffusion Model0
Dual-Representation Interaction Driven Image Quality Assessment with Restoration AssistanceCode0
Modality-Incremental Learning with Disjoint Relevance Mapping Networks for Image-based Semantic Segmentation0
DreamMix: Decoupling Object Attributes for Enhanced Editability in Customized Image InpaintingCode2
MAT: Multi-Range Attention Transformer for Efficient Image Super-ResolutionCode1
Maximally Separated Active Learning0
MARVEL-40M+: Multi-Level Visual Elaboration for High-Fidelity Text-to-3D Content CreationCode0
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