cs231n

Course assignments of CS231n: Convolutional Neural Networks for Visual Recognition

Assignment #1: Image Classification, kNN, SVM, Softmax, Neural Network

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

Q4: Two-Layer Neural Network

• Test accuracy on CIFAR-10: 0.501

Q5: Higher Level Representations: Image Features

• Test accuracy on CIFAR-10: 0.576

Assignment #2: Fully-Connected Nets, Batch Normalization, Dropout, Convolutional Nets

Q1: Fully-connected Neural Network

• Validation / test accuracy on CIFAR-10: 0.547 / 0.539

Q2: Batch Normalization

Q3: Dropout

Q4: Convolutional Networks

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)

Q5: Generative Adversarial Networks