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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 4576–4600 of 9051 papers

TitleStatusHype
Person Image Synthesis via Denoising Diffusion ModelCode2
SinDiffusion: Learning a Diffusion Model from a Single Natural ImageCode2
The Euclidean Space is Evil: Hyperbolic Attribute Editing for Few-shot Image GenerationCode1
FLEX: Full-Body Grasping Without Full-Body Grasps—0
SinFusion: Training Diffusion Models on a Single Image or VideoCode1
Beyond Attentive Tokens: Incorporating Token Importance and Diversity for Efficient Vision TransformersCode0
Evolutionary Strategies for the Design of Binary Linear Codes—0
Plug and Play Active Learning for Object DetectionCode1
Automating Rigid Origami DesignCode1
MEESO: A Multi-objective End-to-End Self-Optimized Approach for Automatically Building Deep Learning Models—0
Cultural Incongruencies in Artificial Intelligence—0
A 2030 United States Macro Grid Unlocking Geographical Diversity to Accomplish Clean Energy Goals—0
An Empirical Study On Contrastive Search And Contrastive Decoding For Open-ended Text GenerationCode1
EDGE: Editable Dance Generation From MusicCode2
ArtELingo: A Million Emotion Annotations of WikiArt with Emphasis on Diversity over Language and Culture—0
Pairwise Instance Relation Augmentation for Long-tailed Multi-label Text Classification—0
Combining State-of-the-Art Models with Maximal Marginal Relevance for Few-Shot and Zero-Shot Multi-Document Summarization—0
Social Diversity Reduces the Complexity and Cost of Fostering Fairness—0
Weighted Ensemble Self-Supervised Learning—0
A Structure-Guided Diffusion Model for Large-Hole Image CompletionCode0
Vision Transformers in Medical Imaging: A Review—0
Contrastive Losses Are Natural Criteria for Unsupervised Video SummarizationCode1
DGRec: Graph Neural Network for Recommendation with Diversified Embedding GenerationCode1
Delving into Transformer for Incremental Semantic Segmentation—0
UniSumm and SummZoo: Unified Model and Diverse Benchmark for Few-Shot SummarizationCode1
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