SOTAVerified

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 1226–1250 of 9051 papers

TitleStatusHype
FacialGAN: Style Transfer and Attribute Manipulation on Synthetic FacesCode1
An Empirical Study of Vehicle Re-Identification on the AI City ChallengeCode1
FaithBench: A Diverse Hallucination Benchmark for Summarization by Modern LLMsCode1
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
Contrastive Quantization with Code Memory for Unsupervised Image RetrievalCode1
Continual Variational Autoencoder Learning via Online Cooperative MemorizationCode1
AMPED: Adaptive Multi-objective Projection for balancing Exploration and skill DiversificationCode1
Feature Likelihood Divergence: Evaluating the Generalization of Generative Models Using SamplesCode1
FedMedICL: Towards Holistic Evaluation of Distribution Shifts in Federated Medical ImagingCode1
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 Image Generation via Cross-domain CorrespondenceCode1
Contrastive Identity-Aware Learning for Multi-Agent Value DecompositionCode1
Contrastive Syn-to-Real GeneralizationCode1
FHDe²Net: Full High Definition Demoireing NetworkCode1
Bias Loss for Mobile Neural NetworksCode1
Blending gradient boosted trees and neural networks for point and probabilistic forecasting of hierarchical time seriesCode1
FII-CenterNet: An Anchor-Free Detector With Foreground Attention for Traffic Object DetectionCode1
Fine-grained Image Classification and Retrieval by Combining Visual and Locally Pooled Textual FeaturesCode1
Fine-Grained VR Sketching: Dataset and InsightsCode1
Amortizing intractable inference in large language modelsCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
Continual Learning for Image Segmentation with Dynamic QueryCode1
AutoMix: Automatically Mixing Language ModelsCode1
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