Data Scarcity

Data scarcity, the limited availability of labeled data for training machine learning models, is a pervasive challenge across numerous scientific domains. Current research focuses on mitigating this limitation through techniques like transfer learning (leveraging knowledge from related datasets), data augmentation (generating synthetic data), and the application of model architectures such as Recurrent Neural Networks (RNNs), Large Language Models (LLMs), and Generative Adversarial Networks (GANs). Overcoming data scarcity is crucial for advancing AI applications in fields like medical imaging, financial modeling, and natural language processing, where acquiring sufficient labeled data is often expensive, time-consuming, or ethically problematic.

Papers

March 14, 2024

Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity
Zhuo Zhi, Ziquan Liu, Moe Elbadawi, Adam Daneshmend, Mine Orlu, Abdul Basit, Andreas Demosthenous, Miguel Rodrigues
Multi Task Multimodal Learning Different Context Missing Modality Data Scarcity Modality Data Missing Modality Data Treasure Trove

March 11, 2024

Leveraging Internal Representations of Model for Magnetic Image Classification
Adarsh N L, Arun P, Alok Porwal, Malcolm Aranha
Deep Learning Full Model Magnetic Resonance Imaging Internal Representation Data Scarcity Training Machine Learning Model Single Reference Image

March 3, 2024

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications
Wei Guo, Fuzhen Zhuang, Xiao Zhang, Yiqi Tong, Jin Dong
Transfer Learning Technical Challenge Financial Application NCD Method Comprehensive Survey Data Scarcity Data Sharing Incremental Data Federated Transfer Learning

February 27, 2024

Comparing effectiveness of regularization methods on text classification: Simple and complex model in data shortage situation
Jongga Lee, Jaeseung Yim, Seohee Park, Changwon Lim
Adversarial Training Semi Supervised Learning Text Classification Regularization Technique Data Scarcity Regularization Effect Complex Model

February 5, 2024

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation
Yuan Gao, Haokun Chen, Xiang Wang, Zhicai Wang, Xue Wang, Jinyang Gao, Bolin Ding
Diffusion Transformer Data Scarcity Audio Sample Effective Augmentation Superior Forecasting

December 15, 2023

Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps
June Moh Goo
Data Scarcity Demographic Attribute Diffusion Map Census Data

December 8, 2023

Data Scarcity in Recommendation Systems: A Survey
Zefeng Chen, Wensheng Gan, Jiayang Wu, Kaixia Hu, Hong Lin
Timely Survey Knowledge Graph Knowledge Transfer Recommendation System Data Scarcity

December 1, 2023

Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation
Aleksi Knuutila
Generative Model Latent Space Synthetic Image StyleGAN Latent Street View Data Scarcity

November 16, 2023

Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model
Raphael Schäfer, Till Nicke, Henning Höfener, Annkristin Lange, Dorit Merhof, Friedrich Feuerhake, Volkmar Schulz, Johannes Lotz, Fabian Kiessling
Foundation Model Supervised ImageNet Multi Task Medical Imaging Data Scarcity Pretrained Model Image Analysis Validation

August 10, 2023

Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning
Ali Cem, Ognjen Jovanovic, Siqi Yan, Yunhong Ding, Darko Zibar, Francesco Da Ros
Transfer Learning Regularization Technique Data Scarcity Photonic Chip Optical Matrix Multiplier Design

July 31, 2023

Generative models for wearables data
Arinbjörn Kolbeinsson, Luca Foschini
Generative Model Data Collection Wearable Sensor Data Data Scarcity Healthcare Data

July 28, 2023

Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding
Jiachen Kang, Wenjing Jia, Xiangjian He, Kin Man Lam
Point Cloud Pre Trained Knowledge Transfer Self Supervised Representation Learning Robust Representation Data Scarcity High Quality Image Point Cloud Understanding 3D Understanding

July 26, 2023

Topologically Regularized Multiple Instance Learning to Harness Data Scarcity
Salome Kazeminia, Carsten Marr, Bastian Rieck
Inductive Bias Multiple Instance Learning Topological Feature Data Scarcity Data Analysis Biomedical Datasets Topological Regularization

June 23, 2023

Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity
Riccardo Presotto, Sannara Ek, Gabriele Civitarese, François Portet, Philippe Lalanda, Claudio Bettini
Training Data Pre Trained Model Supervised Learning Activity Recognition Data Scarcity Unseen Data

May 31, 2023

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation
Adrian Shuai Li, Elisa Bertino, Rih-Teng Wu, Ting-Yan Wu
Deep Learning Domain Adaptation Data Augmentation Data Scarcity Wafer Defect Novel Domain Adaptation Imbalanced Training

May 25, 2023

Scaling Data-Constrained Language Models
Niklas Muennighoff, Alexander M. Rush, Boaz Barak, Teven Le Scao, Aleksandra Piktus, Nouamane Tazi, Sampo Pyysalo, Thomas Wolf, Colin Raffel
Language Model Training Data Information Redundancy Data Scarcity Low Data Regime

May 24, 2023

A Human-in-the-Loop Approach for Information Extraction from Privacy Policies under Data Scarcity
Michael Gebauer, Faraz Maschhur, Nicola Leschke, Elias Grünewald, Frank Pallas
Information Extraction Privacy Policy Human in the Loop Data Scarcity Interpretable Representation Influential Data

May 22, 2023

Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training
Miriam Anschütz, Joshua Oehms, Thomas Wimmer, Bartłomiej Jezierski, Georg Groh
Language Model Text Simplification Textual Feature Data Scarcity

April 7, 2023

Leveraging GANs for data scarcity of COVID-19: Beyond the hype
Hazrat Ali, Christer Gronlund, Zubair Shah
Generative Adversarial Network Covid 19 Computed Tomography Data Scarcity GAN Based HyPe GT

February 22, 2023

Data Augmentation for Neural NLP
Domagoj Pluščec, Jan Šnajder
Natural Language Processing Deep Learning Model Data Augmentation Data Scarcity Neural NLP State of the Art Data

Data Scarcity

Papers

Borrowing Treasures from Neighbors: In-Context Learning for Multimodal Learning with Missing Modalities and Data Scarcity

Leveraging Internal Representations of Model for Magnetic Image Classification

A Comprehensive Survey of Federated Transfer Learning: Challenges, Methods and Applications

Comparing effectiveness of regularization methods on text classification: Simple and complex model in data shortage situation

DiffsFormer: A Diffusion Transformer on Stock Factor Augmentation

Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps

Data Scarcity in Recommendation Systems: A Survey

Generative models for visualising abstract social processes: Guiding streetview image synthesis of StyleGAN2 with indices of deprivation

Overcoming Data Scarcity in Biomedical Imaging with a Foundational Multi-Task Model

Addressing Data Scarcity in Optical Matrix Multiplier Modeling Using Transfer Learning

Generative models for wearables data

Point Clouds Are Specialized Images: A Knowledge Transfer Approach for 3D Understanding

Topologically Regularized Multiple Instance Learning to Harness Data Scarcity

Combining Public Human Activity Recognition Datasets to Mitigate Labeled Data Scarcity

Building Manufacturing Deep Learning Models with Minimal and Imbalanced Training Data Using Domain Adaptation and Data Augmentation

Scaling Data-Constrained Language Models

A Human-in-the-Loop Approach for Information Extraction from Privacy Policies under Data Scarcity

Language Models for German Text Simplification: Overcoming Parallel Data Scarcity through Style-specific Pre-training

Leveraging GANs for data scarcity of COVID-19: Beyond the hype

Data Augmentation for Neural NLP