Course assignments of CS231n: Convolutional Neural Networks for Visual Recognition
Q1: k-Nearest Neighbor classifier
- Test accuracy on CIFAR-10: 0.282
Q2: Training a Support Vector Machine
- Test accuracy on CIFAR-10: 0.376
Q3: Implement a Softmax classifier
- Test accuracy on CIFAR-10: 0.355
- Test accuracy on CIFAR-10: 0.501
Q5: Higher Level Representations: Image Features
- Test accuracy on CIFAR-10: 0.576
Q1: Fully-connected Neural Network
- Validation / test accuracy on CIFAR-10: 0.547 / 0.539
Q3: Dropout
Q5: PyTorch / TensorFlow on CIFAR-10 (Tweaked TF model)
- Training / validation / test accuracy of TF implementation on CIFAR-10: 0.928 / 0.801 / 0.822
- PyTorch implementation:
| Model | Training Accuracy | Test Accuracy |
|---|---|---|
| Base network | 92.86 | 88.90 |
| VGG-16 | 99.98 | 93.16 |
| VGG-19 | 99.98 | 93.24 |
| ResNet-18 | 99.99 | 93.73 |
| ResNet-101 | 99.99 | 93.76 |
Assignment #3: Image Captioning with Vanilla RNNs, Image Captioning with LSTMs, Network Visualization, Style Transfer, Generative Adversarial Networks
Q1: Image Captioning with Vanilla RNNs
Q2: Image Captioning with LSTMs
Q3: Network Visualization: Saliency maps, Class Visualization, and Fooling Images
Q4: Style Transfer (TensorFlow / PyTorch)