This repository contains my homework submissions for the "Deep Learning for Computer Vision" course at National Taiwan University (NTU), taught by Frank Wang.
1. Image Classification
- Train a CNN model from scratch
- Try alternative models/methods (e.g., fine-tune a pre-trained model)
2. Image Segmentation
- VGG16 + FCN32s (baseline model)
- Improved model
-
Image Generation
- Problem 1: GAN [face dataset - CelebA]
- Problem 2: Diffusion models [digit dataset - MNIST-M]
-
Unsupervised Domain Adaptation (UDA)
- Problem 3: DANN [digit dataset - MNIST-M, SVHN and USPS]
-
Problem 1: Zero-shot image classification with CLIP
-
Problem 2: Image Captioning with Vision and Language Model
-
Problem 3: Visualization of Attention in Image Captioning
-
Problem 1: 3D Novel View Synthesis
-
Problem 2: Self-supervised Pre-training for Image Classification