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Related Experiment Videos

Convolutional Analysis Operator Learning: Dependence on Training Data.

Il Yong Chun¹, David Hong¹, Ben Adcock²

¹Department of Electrical Engineering and Computer Science, The University of Michigan, Ann Arbor, MI 48019 USA.

IEEE Signal Processing Letters

|April 22, 2020

Summary

This summary is machine-generated.

Increasing dataset size in Convolutional Analysis Operator Learning (CAOL) training can improve filter accuracy. This study provides theoretical bounds and empirical evidence demonstrating the benefits of using more training data for unsupervised operator learning.

Related Experiment Videos

Area of Science:

Machine Learning
Signal Processing
Computer Vision

Background:

Convolutional Analysis Operator Learning (CAOL) facilitates unsupervised training of convolutional sparsifying operators and autoencoders.
The impact of large dataset sizes on CAOL training outcomes remains incompletely understood.

Purpose of the Study:

To investigate the effect of training dataset size on filter updates in CAOL.
To provide theoretical insights into the relationship between data quantity and learning accuracy.

Main Methods:

Derivation of deterministic and probabilistic bounds on filter estimation errors.
Analysis of how these bounds change with an increasing number of training samples.
Empirical validation using real-world datasets.

Main Results:

A general deterministic bound on errors in estimated filters was established.
Bounds on expected and high-probability errors were derived as a function of dataset size.
Empirical investigations confirmed the theoretical findings with real data.

Conclusions:

The study provides theoretical guarantees and empirical support for the benefits of using larger datasets in CAOL.
Increased training data is shown to potentially lead to more accurate filter learning in unsupervised operator learning.