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Dynamic Perturbation of Weights for Improved Data Reconstruction in Unsupervised Learning.

Manar D Samad1, Rahim Hossain2, Khan M Iftekharuddin3

  • 1Dept. of Computer Science, Tennessee State University, Nashville, TN, USA.

Proceedings of ... International Joint Conference on Neural Networks. International Joint Conference on Neural Networks
|September 26, 2022
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Weight pruning enhances unsupervised autoencoder learning by perturbing model weights, improving data reconstruction and compressing models. This method, applied to non-imaging data, identifies informative weights for better representation.

Keywords:
activation functionautoencoderdata reconstructionmodel compressionweight maskingweight pruning

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Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Data Science

Background:

  • Weight pruning is established for neural network compression in supervised learning.
  • Its application in unsupervised learning, particularly for autoencoders with non-imaging data, remains underexplored.

Purpose of the Study:

  • To investigate the efficacy of weight pruning for unsupervised autoencoder models using non-imaging data.
  • To propose a deterministic algorithm for model perturbation to improve data reconstruction and compression.

Main Methods:

  • Adapting weight pruning for autoencoder weight dynamics during data reconstruction.
  • Developing a deterministic model perturbation algorithm using weight statistics and binary masks.
  • Experimenting across eight diverse non-imaging datasets (e.g., gene sequences, swarm behavior).

Main Results:

  • Periodic weight perturbations improved autoencoder data reconstruction accuracy and model compression.
  • A small fraction (<5%) of weights were found to be highly informative, outperforming negative weights (>90%).
  • Perturbing low or negative weights generally reduced data reconstruction loss.

Conclusions:

  • Weight pruning is a viable technique for enhancing unsupervised autoencoder performance on non-imaging data.
  • The proposed perturbation method offers a deterministic approach to optimize autoencoder behavior for better data representation.
  • This work contributes to understanding and correcting dynamic behaviors in neural networks for improved reconstruction and latent variable discovery.