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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 4226–4250 of 9051 papers

TitleStatusHype
Prompt-Based Exemplar Super-Compression and Regeneration for Class-Incremental LearningCode0
COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics—0
Spectral and Polarization Vision: Spectro-polarimetric Real-world Dataset—0
An Interventional Perspective on Identifiability in Gaussian LTI Systems with Independent Component AnalysisCode0
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models—0
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop—0
The curse of language biases in remote sensing VQA: the role of spatial attributes, language diversity, and the need for clear evaluation—0
HD Maps are Lane Detection Generalizers: A Novel Generative Framework for Single-Source Domain Generalization—0
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering—0
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models—0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor NetworksCode0
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
GAIA: Zero-shot Talking Avatar Generation—0
CUCL: Codebook for Unsupervised Continual LearningCode0
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios—0
Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography—0
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles—0
Touring sampling with pushforward maps—0
Enhancing Task-Oriented Dialogues with Chitchat: a Comparative Study Based on Lexical Diversity and DivergenceCode0
Class Balanced Dynamic Acquisition for Domain Adaptive Semantic Segmentation using Active Learning—0
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