Addressing annotation and data scarcity when designing machine ...

Addressing Medical Imaging Limitations with Synthetic Data ...

Latent diffusion models (LDMs) have emerged as a powerful tool within generative machine learning, particularly for synthesizing 3D medical ...

Chao Zhang

Data-Efficient AI – Adapting Large Language Models for target domains by addressing data scarcity challenges through data-efficient methods such as feedback ...

Developing Computer Vision Applications in Data Scarce ... - Ai4

The crux of these advancements hinges on the availability of vast and varied datasets, essential for training accurate and reliable machine ...

Counteracting Data Scarcity in MedTech - Hochschule Luzern

A major barrier to integrating machine learning into healthcare is the low availability of high-quality annotated medical data. Data of adequate quality and ...

Addressing the data bottleneck in medical deep learning models ...

Any machine learning (ML) model is highly dependent on the data it uses for learning, and this is even more important in the case of deep ...

Data Collection for Machine Learning: The Complete Guide - Waverley

In addition, to lower costs, synthetic data helps address privacy issues associated with potentially sensitive data sourced from the real world.

The Critical Role of Data Annotation in Shaping the Future of ...

Synthetic data generation is emerging as a solution to data scarcity and privacy concerns. Generative models create synthetic datasets, reducing ...

One shot ML Annotations, and Vector Similarity Search - Ubiai

In response to these challenges, machine learning researchers have developed more efficient annotation methods that reduce the amount of data needed to train ...

What is Synthetic Data? - Gretel.ai

Reducing bias in machine learning datasets – enables the creation of more representative and inclusive datasets that can better predict trends, for example, ...

Data Gaps (Beta) - Climate Change AI

One of the foremost challenges is the scarcity of publicly open and adequately annotated data for model training. ... address the lack of publicly open annotated ...

Open Artificial Knowledge (OAK) Dataset: A Large-Scale Resource ...

However, acquiring such datasets presents significant challenges, including data scarcity, privacy concerns, and high data collection and ...

What Is Few-Shot Learning? - IBM

Some FSL methods can used alongside other solutions that address scarcity of labeled data ... Though a wide variety of machine learning model architectures ...

Designing a Data Processing Pipeline - Exponent

Targeted data collection: selecting high value-add datapoints for labeling is an excellent way to mitigate data scarcity. The Pareto principle often applies to ...

Data Annotation Ultimate Guide: Challenges and Solutions - VinBrain

In the rapidly evolving field of artificial intelligence (AI), the success of machine learning projects is heavily dependent on the quality of ...

Volume 1 | Data-centric Machine Learning Research - DMLR

Volume 1 · Building Better Datasets: Seven Recommendations for Responsible Design from Dataset Creators · Benchmarking Robustness of Multimodal Image-Text Models ...

Machine Learning Data Practices through a Data Curation Lens

We propose ways to address these challenges and develop an overall framework for evaluation that outlines how data curation concepts and methods ...

PhD Thesis: Exploiting sensor and process characteristics to tackle ...

In summary, label scarcity stands out as a primary challenge in developing effective AIoT sensing applications. Existing solutions for addressing the label ...

Tackling Data Scarcity Challenge through Active Learning in ...

Machine learning (ML) has been harnessed as a promising modelling tool for materials research. However, small data, or data scarcity, ...

Elevating ML: AI-Driven Data Annotation with LabelGPT

Data labeling is a crucial step in the process of training machine learning and artificial intelligence models. It involves annotating or ...

Navigating Data Scarcity using Foundation Models: A Benchmark of ...

The paper explores the potential of foundation models, which are large pre-trained machine learning models, to address the challenge of data ...