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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 14761500 of 9051 papers

TitleStatusHype
Keyphrase Generation with Cross-Document AttentionCode1
Landmark Detection and 3D Face Reconstruction for Caricature using a Nonlinear Parametric ModelCode1
Exclusive Hierarchical Decoding for Deep Keyphrase GenerationCode1
Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data AugmentationCode1
AREDSUM: Adaptive Redundancy-Aware Iterative Sentence Ranking for Extractive Document SummarizationCode1
BLEU might be Guilty but References are not InnocentCode1
Orthogonal Over-Parameterized TrainingCode1
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
A Sentence Cloze Dataset for Chinese Machine Reading ComprehensionCode1
Evaluating the Evaluation of Diversity in Natural Language GenerationCode1
Sparse Text GenerationCode1
M2m: Imbalanced Classification via Major-to-minor TranslationCode1
Adversarial Feature Hallucination Networks for Few-Shot LearningCode1
Learning Memory-guided Normality for Anomaly DetectionCode1
Variational Transformers for Diverse Response GenerationCode1
Lightweight Photometric Stereo for Facial Details RecoveryCode1
Towards Discriminability and Diversity: Batch Nuclear-norm Maximization under Label Insufficient SituationsCode1
FFR V1.0: Fon-French Neural Machine TranslationCode1
Egoshots, an ego-vision life-logging dataset and semantic fidelity metric to evaluate diversity in image captioning modelsCode1
Two-stage Discriminative Re-ranking for Large-scale Landmark RetrievalCode1
BoostTree and BoostForest for Ensemble LearningCode1
GIQA: Generated Image Quality AssessmentCode1
DLow: Diversifying Latent Flows for Diverse Human Motion PredictionCode1
Multimodal Shape Completion via Conditional Generative Adversarial NetworksCode1
Context-Transformer: Tackling Object Confusion for Few-Shot DetectionCode1
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