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In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Related Experiment Video

Updated: Sep 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

633

Deep representation learning using layer-wise VICReg losses.

Joy Datta1, Rawhatur Rabbi1, Puja Saha2

  • 1Department of Computer Science and Engineering, School of Data and Sciences, Brac University, Dhaka, Bangladesh.

Scientific Reports
|July 27, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a layer-wise neural network training method using Variance-Invariance-Covariance Regularization (VICReg) loss. This approach improves classification accuracy, especially with limited labeled data, by creating compact and informative feature representations.

Keywords:
BackpropagationForward-forward algorithmLayer-wise trainingNeural networksVICReg

Related Experiment Videos

Last Updated: Sep 8, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

633

Area of Science:

  • Machine Learning
  • Deep Learning
  • Computer Vision

Background:

  • Deep Neural Networks (DNNs) often struggle with vanishing gradients and initialization sensitivity.
  • Training DNNs typically requires large amounts of annotated data, which can be scarce.
  • Backpropagation, the standard training method, involves one forward and one backward pass.

Purpose of the Study:

  • To present a novel layer-wise training procedure for neural networks.
  • To address challenges in training DNNs, particularly with limited annotated data.
  • To enhance feature representation learning and classification accuracy.

Main Methods:

  • A layer-wise training procedure minimizing Variance-Invariance-Covariance Regularization (VICReg) loss at each layer.
  • Utilizing two forward passes with original and augmented data instead of backpropagation.
  • Employing a pyramidal network architecture for effective feature extraction.
  • Optimizing weights for variance, invariance, and covariance terms for semantic information capture.

Main Results:

  • The procedure progressively constructs compact and informative feature spaces.
  • Improved classification accuracy on MNIST (7%), EMNIST (16%), Fashion MNIST (1%), and CIFAR-100 (7%) compared to baseline models.
  • Learned representations were assessed using clustering quality metrics and few-shot classification tasks.

Conclusions:

  • The proposed VICReg layer-wise training enhances DNN performance, especially in low-data regimes.
  • This method offers a viable alternative to backpropagation, mitigating common training issues.
  • The approach effectively learns robust and informative representations for downstream tasks.