Contributing Dimension Structure for Coreset Selection in Deep Learning

核心概念

The author introduces a novel Contributing Dimension Structure (CDS) metric to enhance diversity in coreset selection methods, addressing the limitations of existing similarity metrics. By integrating the CDS constraint, the proposed method effectively boosts model performance across various datasets.

要約

The content discusses the importance of coreset selection in deep learning and introduces a novel Contributing Dimension Structure (CDS) metric to improve diversity within selected subsets. Existing methods are critiqued for their inability to adequately capture diversity, leading to sub-optimal model performance. The proposed CDS metric and constraint aim to address this issue by enhancing diversity in coreset selection methods.
Key points highlighted include:

Coreset selection aims to choose crucial training samples efficiently.
Existing methods measure representation and diversity based on similarity metrics like L2-norm.
The proposed CDS metric considers disparities among dimensions contributing significantly to final similarity.
The CDS constraint enhances diversity within selected subsets, improving model performance.
Experimental results demonstrate the effectiveness of the proposed method across different datasets and sampling rates.
The study emphasizes the significance of capturing diverse data representations for efficient machine learning models.

統計

Budget-sized samples will be selected based on importance to form a coreset that can be used for data-efficient machine learning.
Extensive experiments on three datasets demonstrate the general effectiveness of the proposed method in boosting existing methods.
Our method achieves the best improvement compared to the baseline method (CRAIG) when K = 10-M-D and β = 1e-4.

引用

"Our key lies in the introduction of a novel Contributing Dimension Structure (CDS) metric."
"Existing methods tend to select data with similar CDS, hindering model performance."
"The proposed CDS metric and constraint aim to augment diversity within coreset selection methods."

抽出されたキーインサイト

Contributing Dimension Structure of Deep Feature for Coreset Selection

by Zhijing Wan,... 場所 arxiv.org 03-05-2024

https://arxiv.org/pdf/2401.16193.pdf

深掘り質問

How can incorporating diverse sample selections impact real-world applications beyond image classification

Incorporating diverse sample selections can have a significant impact on real-world applications beyond image classification. For instance:

Healthcare: In medical research, selecting a diverse set of patient data for analysis could lead to more robust and generalizable models for disease diagnosis and treatment prediction.
Finance: When building predictive models for financial markets, incorporating diverse samples can help in identifying patterns across different market conditions and improving the accuracy of forecasting.
Natural Language Processing (NLP): In NLP tasks such as sentiment analysis or language translation, diverse sample selection can enhance model performance by capturing a wider range of linguistic nuances and contexts.

What potential challenges or criticisms might arise from implementing the CDS metric and constraint in practical scenarios

Implementing the CDS metric and constraint in practical scenarios may face several challenges or criticisms:

Computational Complexity: Calculating the Contributing Dimension Structure (CDS) metric for each data point may increase computational overhead, especially with large datasets.
Interpretability: The interpretation of contributing dimensions within the CDS metric might be complex, making it challenging to explain model decisions based on these metrics.
Generalization: There could be concerns about how well the CDS metric generalizes across different types of datasets or domains, impacting its applicability in various real-world settings.

How could advancements in coreset selection methodologies influence broader research areas outside of deep learning

Advancements in coreset selection methodologies can have broader implications outside deep learning:

Data Mining: Improved coreset selection techniques can enhance data summarization methods, leading to more efficient processing of large-scale datasets in various industries.
Optimization Algorithms: Coreset selection principles could be integrated into optimization algorithms to accelerate convergence rates and reduce computational costs in solving complex problems.
Machine Learning Theory: Developments in coreset selection methodologies contribute to advancing theoretical foundations related to subset selection strategies and their impact on model training efficiency.