TL;DR
Deeper modern networks outperform the older AlexNet by a wide margin on CIFAR-10, and even a relatively compact ResNet can nearly match the accuracy of a much larger VGG16 in far less time.
In this paper, we perform a systematic empirical study of deep learning techniques on the CIFAR-10 image classification benchmark. We study three inter-related aspects of neural network design: (1) the relative impact of nine gradient descent optimisation algorithms on a baseline Multi-Layer Perceptron (MLP); (2) the e...
A comparative study of different deep learning architectures, including classical CNNs, deep hierarchical models, residual and dense networks, and compound-scaled architectures is presented, showing that deeper networks provide better representation, while residual connections and compound scaling improve training stab...
The results demonstrate that transfer learning significantly improves convergence speed, generalization, and computational efficiency, making it a promising approach for AI applications across domains such as healthcare, NLP, and autonomous systems.
It is demonstrated that hyperparameter optimization dynamics depend heavily on dataset complexity, where computational efficiency is the primary differentiator for simpler classification tasks, but optimization architecture selection becomes critical for navigating challenging medical imaging applications.
A simple fusion of two novel components of residual attentive information forms a robust volume of selective residual attentive patterns (named SRAP), which boosted the performance of lightweight CNN-based networks by up to ~7% on ImageNet-100 without increasing the computational complexity.
Findings indicate a trade-off between computational efficiency and predictive performance under the experimental conditions used and show that the fine-tuned VGG16 model consistently outperformed the plain CNN across the principal performance measures.
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