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

Showing 601625 of 9051 papers

TitleStatusHype
Diverse Weight Averaging for Out-of-Distribution GeneralizationCode1
Diverse Semantic Image Synthesis via Probability Distribution ModelingCode1
Diversified Adversarial Attacks based on Conjugate Gradient MethodCode1
Diversifying Dialog Generation via Adaptive Label SmoothingCode1
Fully Unsupervised Diversity Denoising with Convolutional Variational AutoencodersCode1
Draw Your Art Dream: Diverse Digital Art Synthesis with Multimodal Guided DiffusionCode1
Diverse Generative Perturbations on Attention Space for Transferable Adversarial AttacksCode1
Diverse Human Motion Prediction Guided by Multi-Level Spatial-Temporal AnchorsCode1
A Diverse Corpus for Evaluating and Developing English Math Word Problem SolversCode1
Improving Semi-supervised Federated Learning by Reducing the Gradient Diversity of ModelsCode1
Diverse Human Motion Prediction via Gumbel-Softmax Sampling from an Auxiliary SpaceCode1
Benchmarking Algorithms for Federated Domain GeneralizationCode1
Diverse, Controllable, and Keyphrase-Aware: A Corpus and Method for News Multi-Headline GenerationCode1
Diverse and Specific Clarification Question Generation with KeywordsCode1
Diverse and Faithful Knowledge-Grounded Dialogue Generation via Sequential Posterior InferenceCode1
Diverse Beam Search: Decoding Diverse Solutions from Neural Sequence ModelsCode1
Diverse Cotraining Makes Strong Semi-Supervised SegmentorCode1
Diverse Image Captioning with Context-Object Split Latent SpacesCode1
DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial NetworkCode1
DivClust: Controlling Diversity in Deep ClusteringCode1
DiveR-CT: Diversity-enhanced Red Teaming Large Language Model Assistants with Relaxing ConstraintsCode1
BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask LearningCode1
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D DetectionCode1
BeLFusion: Latent Diffusion for Behavior-Driven Human Motion PredictionCode1
A Closer Look at Machine Unlearning for Large Language ModelsCode1
Show:102550
← PrevPage 25 of 363Next →

No leaderboard results yet.