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Particle Swarm Optimization-Based Transfer Learning for Image Segmentation and Classification
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DOI：10.4018/IJSIR.400762.png)
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Abstract
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Appropriate model hyperparameters, transfer learning strategies, and loss balancing are still challenging to discover, while deep learning has achieved remarkable progress in picture segmentation and classification. An integrated encoder-decoder network is improved in this transfer learning system with the application of Particle Swarm Optimization (PSO). To get hierarchical data, a pretrained encoder is used, and lightweight segmentation and classification heads learn two tasks at once. By default, PSO, a universal meta-optimizer, changes learning rates, layer-freezing masks, batch sizes, and loss-weighting coefficients. Manual searches are accelerated in this way. With this hybrid approach, we may improve the accuracy-efficiency trade-off by combining global exploration with gradient descent and Particle Swarm Optimization (PSO). Cityscapes, PASCAL VOC, and CIFAR simulations demonstrate that swarm-based meta-learning has the potential to increase the flexibility of transfer learning, leading to more efficient and less expensive models for picture processing.
Keywords:
Particle Swarm Optimization
Transfer Learning
Image Segmentation
Image Classification
Meta-Learning
Multi-Objective Optimization
Encoder-Decoder Networks
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