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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 1011–1020 of 9051 papers

TitleStatusHype
Controllable Group Choreography using Contrastive DiffusionCode1
Parameter Efficient Adaptation for Image Restoration with Heterogeneous Mixture-of-ExpertsCode1
Cooperative Open-ended Learning Framework for Zero-shot CoordinationCode1
CtrSVDD: A Benchmark Dataset and Baseline Analysis for Controlled Singing Voice Deepfake DetectionCode1
Deep Encoder-Decoder Networks for Classification of Hyperspectral and LiDAR DataCode1
DiffStega: Towards Universal Training-Free Coverless Image Steganography with Diffusion ModelsCode1
Analyzing Generalization of Vision and Language Navigation to Unseen Outdoor AreasCode1
On Disentangling Spoof Trace for Generic Face Anti-SpoofingCode1
One-Shot Neural Ensemble Architecture Search by Diversity-Guided Search Space ShrinkingCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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