基於深度學習之視覺辨識專論(英文授課)
Selected Topics in Visual Recognition using Deep Learning
| 節 | 週四 |
|---|---|
3 10:10–11:00 | 基於深度學習之視覺辨識專論(英文授課) EC114 3 節連堂 |
4 11:10–12:00 | |
N 12:20–13:10 |
* 根據陽明交大上課時間表所列
Computer vision aims to enable computers to see, understand, and interpret the world like human visual systems. Deep learning technologies are at the core of the current computer vision revolution. Large-scale annotated data and affordable GPU hardware jointly allow the training of deep learning models with hundreds of layers and millions of parameters, which greatly improve the performance of various machine vision applications and even initiate new vision applications. In the course, I will first introduce some deep learning technologies that are widely used in computer vision research, including deep neural networks, convolutional neural networks, and generative adversarial networks. Then, I will cover some important vision applications such as object recognition, detection, and segmentation, and the corresponding advanced deep learning algorithms.
1. Basic knowledge of linear algebra and calculus 2. Programming experience such as Python 3. Deep learning programming skills such as Pytorch, Keras, or TensorFlow
Four homework assignments 72% (=18% x 4) Final project 28%
| 週次 | 主題 |
|---|---|
| 第 1 週 | Introduction to Computer Vision |
| 第 2 週 | Conventional Machine Learning: AdaBoost for Face Detection |
| 第 3 週 | Deep Neural Networks and Convolutional Neural Networks |
| 第 4 週 | Representative CNN Architectures: AlexNet, VGG-Net, GoogleNet, ResNet, and DenseNet |
| 第 5 週 | Transformers for Computer Vision |
| 第 6 週 | Object detection I: R-CNN, Fast R-CNN, Faster-RCNN, and YOLO |
| 第 7 週 | Object detection II: SSD, FCOS, and DETR and Its Variants |
| 第 8 週 | Generative Adversarial Learning : GAN, cGAN, and CycleGAN |
| 第 9 週 | Semantic Segmentation and Instance Segmentation |
| 第 10 週 | Segmentation with Few Training Data Annotations |
| 第 11 週 | Image Super-resolution |
| 第 12 週 | Image and Video Processing: Style Transfer, Video Frame Interpolation, and Video Synthesis |
| 第 13 週 | Video Understanding |
| 第 14 週 | 3D Point Cloud |
| 第 15 週 | Guest Lectures (tentative) |
| 第 16 週 | Final Project Presentation I |
| 第 17 週 | Final Project Presentation II |
Ian Goodfellow, Yoshua Bengio, and Aaron Courville, Deep Learning, MIT Press, 2016 Richard Szeliski, Computer Vision: Algorithms and Applications, Springer Verlag London, 2011.
- 地點
- Online: Please send an email to TAs for making an appointment in advance
- 時間
- Thursday 2:00 pm ~ 3:00 pm
- 聯絡方式
- Instructor and Email: Yen-Yu Lin (林彥宇) lin@cs.nctu.edu.tw TAs and Emails: Jimmy Yang (楊証琨) d08922002@csie.ntu.edu.tw Po-Sheng Liu (劉柏聲) bensonliu0904@gmail.com Chen-Hsuan Tai (戴晨軒) derekt.cs06@nctu.edu.tw Cheng-Ju Ho (何政儒) ace52751208@gmail.com