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HybridSOMSpikeNet: A Deep Model with Differentiable Soft Self-Organizing Maps and Spiking Dynamics for Waste Classification

Debojyoti Ghosh, Adrijit Goswami

TL;DR

HybridSOMSpikeNet addresses the waste classification problem by uniting a deep CNN feature extractor with a differentiable Soft Self-Organizing Map and a temporal Spiking Head. The approach achieves a test accuracy of $97.39\%$ on a 10-class waste dataset, outperforming several CNN baselines while maintaining a modest CPU-friendly footprint. Key contributions include the first differentiable SOM integrated end-to-end with a ResNet-152 backbone and a spiking head, enabling topological feature learning and temporally robust predictions. The work advances sustainable AI by delivering accurate, energy-efficient waste classification suitable for smart bins and edge deployments, with positive implications for recycling efficiency and environmental impact.

Abstract

Accurate waste classification is vital for achieving sustainable waste management and reducing the environmental footprint of urbanization. Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions. To address these issues, this study introduces HybridSOMSpikeNet, a hybrid deep learning framework that integrates convolutional feature extraction, differentiable self-organization, and spiking-inspired temporal processing to enable intelligent and energy-efficient waste classification. The proposed model employs a pre-trained ResNet-152 backbone to extract deep spatial representations, followed by a Differentiable Soft Self-Organizing Map (Soft-SOM) that enhances topological clustering and interpretability. A spiking neural head accumulates temporal activations over discrete time steps, improving robustness and generalization. Trained on a ten-class waste dataset, HybridSOMSpikeNet achieved a test accuracy of 97.39%, outperforming several state-of-the-art architectures while maintaining a lightweight computational profile suitable for real-world deployment. Beyond its technical innovations, the framework provides tangible environmental benefits. By enabling precise and automated waste segregation, it supports higher recycling efficiency, reduces contamination in recyclable streams, and minimizes the ecological and operational costs of waste processing. The approach aligns with global sustainability priorities, particularly the United Nations Sustainable Development Goals (SDG 11 and SDG 12), by contributing to cleaner cities, circular economy initiatives, and intelligent environmental management systems.

HybridSOMSpikeNet: A Deep Model with Differentiable Soft Self-Organizing Maps and Spiking Dynamics for Waste Classification

TL;DR

HybridSOMSpikeNet addresses the waste classification problem by uniting a deep CNN feature extractor with a differentiable Soft Self-Organizing Map and a temporal Spiking Head. The approach achieves a test accuracy of on a 10-class waste dataset, outperforming several CNN baselines while maintaining a modest CPU-friendly footprint. Key contributions include the first differentiable SOM integrated end-to-end with a ResNet-152 backbone and a spiking head, enabling topological feature learning and temporally robust predictions. The work advances sustainable AI by delivering accurate, energy-efficient waste classification suitable for smart bins and edge deployments, with positive implications for recycling efficiency and environmental impact.

Abstract

Accurate waste classification is vital for achieving sustainable waste management and reducing the environmental footprint of urbanization. Misclassification of recyclable materials contributes to landfill accumulation, inefficient recycling, and increased greenhouse gas emissions. To address these issues, this study introduces HybridSOMSpikeNet, a hybrid deep learning framework that integrates convolutional feature extraction, differentiable self-organization, and spiking-inspired temporal processing to enable intelligent and energy-efficient waste classification. The proposed model employs a pre-trained ResNet-152 backbone to extract deep spatial representations, followed by a Differentiable Soft Self-Organizing Map (Soft-SOM) that enhances topological clustering and interpretability. A spiking neural head accumulates temporal activations over discrete time steps, improving robustness and generalization. Trained on a ten-class waste dataset, HybridSOMSpikeNet achieved a test accuracy of 97.39%, outperforming several state-of-the-art architectures while maintaining a lightweight computational profile suitable for real-world deployment. Beyond its technical innovations, the framework provides tangible environmental benefits. By enabling precise and automated waste segregation, it supports higher recycling efficiency, reduces contamination in recyclable streams, and minimizes the ecological and operational costs of waste processing. The approach aligns with global sustainability priorities, particularly the United Nations Sustainable Development Goals (SDG 11 and SDG 12), by contributing to cleaner cities, circular economy initiatives, and intelligent environmental management systems.
Paper Structure (48 sections, 18 equations, 5 figures, 5 tables, 4 algorithms)

This paper contains 48 sections, 18 equations, 5 figures, 5 tables, 4 algorithms.

Table of Contents

  1. Introduction
  2. Literature Survey
  3. Deep CNN-Based Models for Garbage Classification
  4. Transfer Learning Techniques and Hybrid Models
  5. Optimization and Lightweight Architectures
  6. Neuro-Inspired and Unsupervised Learning Models
  7. Recent Innovations in Sustainable Waste Management
  8. Novelty and Effectiveness of HybridSOMSpikeNet
  9. Dataset Overview and Preprocessing
  10. Dataset Overview
  11. Dataset Splitting Strategy
  12. Data Transformations
  13. Training Transformations:
  14. Validation and Test Transformations:
  15. Data Loader Configuration
  16. ...and 33 more sections

Figures (5)

  • Figure 1: Sample images from the curated garbage classification dataset.
  • Figure 2: Diagram of HybridSOMSpikeNet architecture showing sequential processing from input to output.
  • Figure 3: Flowchart of the Soft Self-Organizing Layer (SSOL). The input batch is passed through a differentiable clustering process involving distance computation, soft assignment, and optional regularization.
  • Figure 4: Flowchart of the Spiking Head module.
  • Figure 5: Training Pipeline for HybridSOMSpikeNet.