Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Force Classification

Force Classification

Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Associative Learning

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Fine-Tuning Convolutional Neural Networks for COVID-19 Detection from Chest X-ray Images.

Diagnostics (Basel, Switzerland)·2021

Same author

Deep learning for COVID-19 detection based on CT images.

Scientific reports·2021

Same author

How to Represent Paintings: A Painting Classification Using Artistic Comments.

Sensors (Basel, Switzerland)·2021

Same author

Compare the performance of the models in art classification.

PloS one·2021

Search research articles

Related Experiment Video

Updated: Sep 20, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Big Transfer Learning for Fine Art Classification.

Wentao Zhao^1,2, Wei Jiang², Xinguo Qiu²

¹School of Intelligent Transportation, Zhejiang Institute of Mechanical & Electrical Engineering, Hangzhou 310053, Zhejiang, China.

Computational Intelligence and Neuroscience

|June 10, 2022

Summary

This summary is machine-generated.

Convolutional neural networks (CNNs) show promise for classifying fine art. Big transfer learning (BiT), a method using pre-trained models, significantly improves art classification accuracy and enables effective image retrieval.

More Related Videos

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Related Experiment Videos

Last Updated: Sep 20, 2025

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images

Published on: October 27, 2023

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Combining Eye-tracking Data with an Analysis of Video Content from Free-viewing a Video of a Walk in an Urban Park Environment

Published on: May 7, 2019

Area of Science:

Computer Vision
Artificial Intelligence
Art History

Background:

Automatic classification and retrieval of fine art are increasingly important.
Convolutional neural networks (CNNs) are powerful tools for image analysis.

Purpose of the Study:

To explore the effectiveness of CNNs for fine art image classification.
To investigate the impact of hyperparameters and transfer learning on model performance.
To introduce and validate a novel transfer learning approach called Big Transfer Learning (BiT).

Main Methods:

Experiments were conducted using CNNs with varying hyperparameters, including image resolution and training steps with mix-up.
A systematic comparison of five weight initializations was performed to evaluate transfer learning.
The Big Transfer Learning (BiT) method involved fine-tuning networks pre-trained on large datasets.

Main Results:

Higher image resolution and optimized training steps with mix-up improved CNN performance.
Fine-tuning pre-trained networks demonstrated superior generalizability, a phenomenon termed Big Transfer Learning (BiT).
The BiT approach achieved state-of-the-art results in fine art classification, outperforming previous methods significantly.
Visualizations aided in understanding the feature extraction mechanisms of the models.
The models successfully retrieved paintings based on image similarity, even for computer-generated art.

Conclusions:

CNNs, particularly with Big Transfer Learning (BiT), are highly effective for fine art classification and retrieval.
Pre-trained models capture generalizable knowledge applicable to the art domain.
The developed models offer insights into AI's interpretation of art and enable new retrieval capabilities.