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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 1551–1600 of 9051 papers

TitleStatusHype
Adaptively Sparse TransformersCode1
Topic Modeling with Wasserstein AutoencodersCode1
Multi-Modal Adversarial Autoencoders for Recommendations of Citations and Subject LabelsCode1
Tracking Holistic Object RepresentationsCode1
Leveraging Knowledge Bases And Parallel Annotations For Music Genre TranslationCode1
Dan: Deep attention neural network for news recommendationCode1
An Optimistic Perspective on Offline Reinforcement LearningCode1
Procedural Content Generation through Quality DiversityCode1
Reinforcement Learning with Convex ConstraintsCode1
SEN12MS -- A Curated Dataset of Georeferenced Multi-Spectral Sentinel-1/2 Imagery for Deep Learning and Data FusionCode1
Large-scale Landmark Retrieval/Recognition under a Noisy and Diverse DatasetCode1
Deep Batch Active Learning by Diverse, Uncertain Gradient Lower BoundsCode1
Generating Diverse High-Fidelity Images with VQ-VAE-2Code1
A Hierarchical Probabilistic U-Net for Modeling Multi-Scale AmbiguitiesCode1
DRIT++: Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
The Curious Case of Neural Text DegenerationCode1
Jointly Measuring Diversity and Quality in Text Generation ModelsCode1
Image Generation From Small Datasets via Batch Statistics AdaptationCode1
DeepHuman: 3D Human Reconstruction from a Single ImageCode1
Dense Relational Captioning: Triple-Stream Networks for Relationship-Based CaptioningCode1
Lattice CNNs for Matching Based Chinese Question AnsweringCode1
Improving Adversarial Robustness via Promoting Ensemble DiversityCode1
AVA-ActiveSpeaker: An Audio-Visual Dataset for Active Speaker DetectionCode1
Towards Accurate Generative Models of Video: A New Metric & ChallengesCode1
WikiHow: A Large Scale Text Summarization DatasetCode1
Diverse Image-to-Image Translation via Disentangled RepresentationsCode1
Open Source Automatic Speech Recognition for GermanCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
CrowdHuman: A Benchmark for Detecting Human in a CrowdCode1
On the effectiveness of task granularity for transfer learningCode1
The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesionsCode1
KonIQ-10k: Towards an ecologically valid and large-scale IQA databaseCode1
Diversity is All You Need: Learning Skills without a Reward FunctionCode1
Texygen: A Benchmarking Platform for Text Generation ModelsCode1
Building a Conversational Agent Overnight with Dialogue Self-PlayCode1
TAC-GAN - Text Conditioned Auxiliary Classifier Generative Adversarial NetworkCode1
Remote Sensing Image Scene Classification: Benchmark and State of the ArtCode1
CityPersons: A Diverse Dataset for Pedestrian DetectionCode1
Unrolled Generative Adversarial NetworksCode1
Conditional Image Synthesis With Auxiliary Classifier GANsCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Frame- and Segment-Level Features and Candidate Pool Evaluation for Video Caption GenerationCode1
Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading BooksCode1
How Many Topics? Stability Analysis for Topic ModelsCode1
Visual Place Recognition for Large-Scale UAV Applications—0
Synthesizing Reality: Leveraging the Generative AI-Powered Platform Midjourney for Construction Worker Detection—0
GEMMAS: Graph-based Evaluation Metrics for Multi Agent Systems—0
Adversarial attacks to image classification systems using evolutionary algorithms—0
Multi-population GAN Training: Analyzing Co-Evolutionary Algorithms—0
Learning What Matters: Probabilistic Task Selection via Mutual Information for Model Finetuning—0
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