Observing Pre-trained CNN Layer as Hand-crafted Features for Detecting Bias in Image Classification Data

Authors

  • Amadea Claire Isabel Ardison Bina Nusantara University
  • Mikhaya Josheba Rumondang Hutagalung Bina Nusantara University
  • Reynaldi Chernando Bina Nusantara University
  • Tjeng Wawan Cenggoro Bina Nusantara University

Keywords:

computer vision, pre-trained CNN layer, hand-crafted features, data bias

Abstract

Detecting bias in data is important since it can pose some serious problems when developing an AI algorithm. This research aims to propose a novel study design to detect bias in image classification data by using pre-trained CNN layers as hand-crafted features. There are 3 datasets used in this research with varying degrees of complexities which are MNIST Digits, Batik collections, and CIFAR-10. The research observed the effect of pre-trained CNN layers especially on individual kernels for feature extraction. By observing the effect of individual kernels the research can better make sense of what is happening inside a CNN layer. The research found that color in the image is an important factor when working with CNN. Furthermore, the proposed study design is also able to detect bias in image classification data where it is related to the color of the image. Detecting this bias early on is important in helping developers improve AI algorithms.

Dimensions

Author Biographies

Amadea Claire Isabel Ardison, Bina Nusantara University

School of Computer Science, Bina Nusantara University
Jakarta 11480, Indonesia

Mikhaya Josheba Rumondang Hutagalung, Bina Nusantara University

School of Computer Science, Bina Nusantara University
Jakarta 11480, Indonesia

Reynaldi Chernando, Bina Nusantara University

School of Computer Science, Bina Nusantara University
Jakarta 11480, Indonesia

Tjeng Wawan Cenggoro, Bina Nusantara University

School of Computer Science, Bina Nusantara University
Jakarta 11480, Indonesia

Bioinformatics and Data Science Research Center, Bina Nusantara University
Jakarta 11480, Indonesia

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Published

2022-06-08
Abstract 54  .