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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 1301–1325 of 9051 papers

TitleStatusHype
ConvNet vs Transformer, Supervised vs CLIP: Beyond ImageNet AccuracyCode1
Control, Generate, Augment: A Scalable Framework for Multi-Attribute Text GenerationCode1
House-GAN: Relational Generative Adversarial Networks for Graph-constrained House Layout GenerationCode1
Boosting Single Image Super-Resolution via Partial Channel ShiftingCode1
How Does It Function? Characterizing Long-term Trends in Production Serverless WorkloadsCode1
How Do Your Code LLMs Perform? Empowering Code Instruction Tuning with High-Quality DataCode1
Boosting Transferability in Vision-Language Attacks via Diversification along the Intersection Region of Adversarial TrajectoryCode1
Diversity is Definitely Needed: Improving Model-Agnostic Zero-shot Classification via Stable DiffusionCode1
Rethinking conditional GAN training: An approach using geometrically structured latent manifoldsCode1
BoostTree and BoostForest for Ensemble LearningCode1
Contrastive Syn-to-Real GeneralizationCode1
Human-M3: A Multi-view Multi-modal Dataset for 3D Human Pose Estimation in Outdoor ScenesCode1
Bootstrapping Referring Multi-Object TrackingCode1
Hybrid CNN-LSTM based Indoor Pedestrian Localization with CSI Fingerprint MapsCode1
CETN: Contrast-enhanced Through Network for CTR PredictionCode1
ID-Booth: Identity-consistent Face Generation with Diffusion ModelsCode1
An Informative Tracking BenchmarkCode1
Illuminating Diverse Neural Cellular Automata for Level GenerationCode1
Controllable and Guided Face Synthesis for Unconstrained Face RecognitionCode1
Image Quality-aware Diagnosis via Meta-knowledge Co-embeddingCode1
Imagining The Road Ahead: Multi-Agent Trajectory Prediction via Differentiable SimulationCode1
I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree SearchCode1
Advanced Codebook Design for SCMA-aided NTNs With Randomly Distributed UsersCode1
Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problemCode1
Contrastive Model Inversion for Data-Free Knowledge DistillationCode1
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