Stop overkilling simple tasks with black-box models and use transparent models instead

Matteo Rizzo, Matteo Marcuzzo, Alessandro Zangari, Andrea Gasparetto, Andrea Albarelli
2/6/2023
cs.LGcs.CV

No official code found. Best implementation: matteo-rizzo/explainable-banana-ripeness-classification (0 stars, MIT, last commit 2025-03-24).

Abstract

In recent years, the employment of deep learning methods has led to several significant breakthroughs in artificial intelligence. Different from traditional machine learning models, deep learning-based approaches are able to extract features autonomously from raw data. This allows for bypassing the feature engineering process, which is generally considered to be both error-prone and tedious. Moreover, deep learning strategies often outperform traditional models in terms of accuracy.

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Code Implementations(1)

matteo-rizzo/explainable-banana-ripeness-classificationCommunity100%
MIT

This repository contains the code related to the paper "Stop overkilling simple tasks with black-box models, use more transparent models instead"

00PythonNov 17, 20221 years agoMIT
classificationconvolutional-neural-networksdata-augmentationfruitfruit-recognition+5 more

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Cite this paper

@article{rizzo2023stop,
  title  = {Stop overkilling simple tasks with black-box models and use transparent models instead},
  author = {Matteo Rizzo and Matteo Marcuzzo and Alessandro Zangari and Andrea Gasparetto and Andrea Albarelli},
  year   = {2023},
  eprint = {2302.02804},
  archivePrefix = {arXiv},
  url    = {http://arxiv.org/abs/2302.02804v3}
}

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