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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 3176–3200 of 9051 papers

TitleStatusHype
DEU-Net: Dual-Encoder U-Net for Automated Skin Lesion SegmentationCode1
Scene Summarization: Clustering Scene Videos into Spatially Diverse Frames—0
Minimax Exploiter: A Data Efficient Approach for Competitive Self-Play—0
Large Language Models Suffer From Their Own Output: An Analysis of the Self-Consuming Training Loop—0
StyleCap: Automatic Speaking-Style Captioning from Speech Based on Speech and Language Self-supervised Learning Models—0
TextDiffuser-2: Unleashing the Power of Language Models for Text Rendering—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
DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models—0
Spatial Diarization for Meeting Transcription with Ad-Hoc Acoustic Sensor NetworksCode0
Exploring Attribute Variations in Style-based GANs using Diffusion Models—0
Metric Space Magnitude for Evaluating the Diversity of Latent RepresentationsCode1
Reinforcement Learning from Diffusion Feedback: Q* for Image Search—0
Efficient Dataset Distillation via Minimax DiffusionCode1
Cerbero-7B: A Leap Forward in Language-Specific LLMs Through Enhanced Chat Corpus Generation and EvaluationCode1
GAIA: Zero-shot Talking Avatar Generation—0
Vector-Quantized Prompt Learning for Paraphrase Generation—0
A Novel Deep Clustering Framework for Fine-Scale Parcellation of Amygdala Using dMRI Tractography—0
RandMSAugment: A Mixed-Sample Augmentation for Limited-Data Scenarios—0
CUCL: Codebook for Unsupervised Continual LearningCode0
Fine-Grained Unsupervised Cross-Modality Domain Adaptation for Vestibular Schwannoma Segmentation—0
Mitigating Shortcut Learning with Diffusion Counterfactuals and Diverse Ensembles—0
Enhancing Task-Oriented Dialogues with Chitchat: a Comparative Study Based on Lexical Diversity and DivergenceCode0
Dialogue Quality and Emotion Annotations for Customer Support ConversationsCode0
Grammatical Error Correction via Mixed-Grained Weighted Training—0
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