ICCV19 Seoul Tutorial : Everything You Need to Know to Reproduce SOTA Deep Learning Models from Hands-on Tutorial (Proposing)
Deep Learning has become the de facto standard algorithm in computer vision. There are a surge amount of approaches being proposed every year for different tasks. Reproducing the complete system in every single detail can be problematic and time-consuming, especially for the beginners. Existing open-source implementations are typically not well-maintained and the code can be easily broken by the rapid updates of the deep learning frameworks. In this tutorial, we will walk through the technical details of the state-of-the-art (SOTA) algorithms in major computer vision tasks, and we also provide the code implementations and hands-on tutorials to reproduce the large-scale training in this tutorial.
| Time | Title | Slides | Notebooks |
|---|---|---|---|
| 8:00-8:15 | Welcome and AWS Setup | link | link |
| 8:15-8:30 | Deep Learning and Gluon Basics (NDArray, AutoGrad, Libraries) | link,link | |
| 8:30-9:30 | Bags of Tricks for Image Classification (ResNet, MobileNet, Inception) | link | link |
| 9:30-10:30 | Understanding Object Detectors (SSD, Faster RCNN, YOLOV3) | link | link |
| 10:30-11:30 | Semantic segmentation algorithms (FCN, PSPNet, DeepLabV3) | link | link |
| 11:30-12:00 | Painless Deployment (C++, TVM) | link,link | |
| 12:00-12:15 | Q&A and Closing |