seunshix / convolutional_neural_networks

Preprocessed the USPS dataset, implemented and compared different network architectures and optimization techniques, applied regularization techniques such as ensembling and dropout, performed adversarial training to evaluate network robustness, and evaluated network performance using metrics such as accuracy, precision, and recall.

Convolutional Neural Networks (CNNs) have shown remarkable success in many computer vision tasks such as image classification, object detection, and segmentation. However, the performance of CNNs can be affected by various factors such as the network architecture, optimization techniques, and the presence of adversarial examples. Therefore, there is a need for a comprehensive analysis of different network architectures and optimization techniques to improve the performance of CNNs.

The goal of this project is to compare and analyze the performance of different network architectures, optimization techniques, and regularization techniques on the USPS dataset. The task involves implementing fully connected neural networks, locally connected neural networks, and convolutional neural networks, applying various optimization techniques, and evaluating the performance of the networks using metrics such as accuracy, precision, and recall.

The project involves several steps, including:

1. Preprocessing the USPS dataset by normalizing the pixel values and dividing it into training and testing sets.
2. Implementing fully connected neural networks, locally connected neural networks, and convolutional neural networks with different architectures.
3. Applying various optimization techniques such as learning rate scheduling, batch optimization, and momentum to improve the performance of the networks.
4. Applying regularization techniques such as ensembling and dropout to improve the generalization of the networks.
5. Performing adversarial training to evaluate the robustness of the networks against adversarial examples.
6. Evaluating the performance of the networks using metrics such as accuracy, precision, and recall.

The project successfully compared and analyzed the performance of different network architectures and optimization techniques on the USPS dataset. The results showed that the convolutional neural network outperformed the other networks in terms of accuracy, precision, and recall. The optimization techniques such as learning rate scheduling, batch optimization, and momentum also improved the performance of the networks. The regularization techniques such as ensembling and dropout improved the generalization of the networks. The adversarial training revealed that the networks are vulnerable to adversarial examples, and further work is needed to improve the robustness of the networks against such examples. The project demonstrated the importance of evaluating different network architectures and optimization techniques to improve the performance of CNNs in computer vision tasks.