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#data-scarcity News & Analysis

8 articles tagged with #data-scarcity. AI-curated summaries with sentiment analysis and key takeaways from 50+ sources.

8 articles
AIBullisharXiv – CS AI · Jun 97/10
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Ego-Pi: VLA Fine-Tuning for Ego-Centric Human and Robot Data

Ego-Pi introduces a fine-tuning approach for the π₀.₅ foundation model that leverages egocentric human manipulation data to train humanoid robots with dexterous hands. The research demonstrates that human demonstrations enable robots to learn new task semantics and compose skills into novel behaviors without requiring robot-specific training data, addressing robotics' persistent data scarcity challenge.

AIBullisharXiv – CS AI · May 117/10
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Overcoming data scarcity through multi-center federated learning for organs-at-risk segmentation in pediatric upper abdominal radiotherapy

Researchers demonstrated that federated learning enables multiple medical centers to collaboratively train pediatric organ segmentation models without sharing sensitive patient data. The approach matched local performance while significantly improving cross-center robustness for CT-based radiotherapy planning, addressing a critical gap in pediatric cancer care where data scarcity has limited model development.

AIBullisharXiv – CS AI · Jun 106/10
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Boosting ECG Classification Performance by Pre-training with Synthesized Data

Researchers developed a knowledge-driven algorithm to generate synthetic ECG data for training deep neural networks, demonstrating that synthetic-to-real pre-training improves abnormal heart rhythm classification by up to 33.2%. This approach addresses the critical challenge of data scarcity in medical AI by leveraging domain-specific knowledge rather than relying solely on difficult-to-obtain real-world patient data.

AINeutralarXiv – CS AI · Jun 96/10
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No Free Lunch for Synthetic Images under Data Scarcity Conditions

Researchers evaluated trade-offs between fidelity, privacy, and utility in synthetic image generation across VAE, GAN, and DDPM models under data scarcity conditions. The study reveals that GANs and DDPMs maintain performance better than VAEs when differential privacy mechanisms are applied, suggesting no single generative model excels across all three dimensions simultaneously.

AINeutralarXiv – CS AI · Jun 16/10
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Target-Side Paraphrase Augmentation for Sign Language Translation with Large Language Models

Researchers demonstrate that GPT-4o-generated paraphrases can improve sign language translation by augmenting training data while keeping video inputs unchanged. Testing across three sign language datasets reveals modest gains on PHOENIX14T (9.56 to 10.33 BLEU-4) but exposes fundamental limitations when data is sparse or highly controlled.

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AINeutralarXiv – CS AI · May 296/10
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An accuracy-aware extension to LRP-based pruning for CNNs to prevent cascading accuracy degradation in data-scarce transfer learning

Researchers propose an accuracy-aware pruning mechanism for CNNs that improves upon existing Layer-wise Relevance Propagation (LRP) methods to reduce model size without degrading performance in transfer learning scenarios with limited data. The approach dynamically adjusts pruning rates using harmonic mean of class accuracy, achieving 15% improvement in compression efficiency while maintaining task-specific accuracy.

AIBullisharXiv – CS AI · Apr 106/10
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Synthetic Homes: A Multimodal Generative AI Pipeline for Residential Building Data Generation under Data Scarcity

Researchers developed a multimodal generative AI pipeline that creates synthetic residential building datasets from publicly available county records and images, addressing critical data scarcity challenges in building energy modeling. The system achieves over 65% overlap with national reference data, enabling scalable energy research and urban simulations without relying on expensive or privacy-restricted datasets.