Главная
Study mode:
on
1
Introduction
2
generative imaging
3
do defy
4
Golden Gate Bridge
5
Porcelain
6
Basic Approach
7
Units
8
Loss Function
9
Gains
10
Moving Images
11
Three Key Approaches
12
Reliable Feature Detection
13
SelfAttention
14
Solution
15
Demonstration
16
Conclusion
17
Salk Institute
18
The eternal triangle of compromise
19
How to image the brain
20
The new strategy
21
Results
22
Data is cleaner
23
False positives
24
Live fluorescence imaging
25
In conclusion
26
Jeremy
Description:
Save Big on Coursera Plus. 7,000+ courses at $160 off. Limited Time Only! Grab it Explore new approaches to image and video reconstruction using deep learning in this F8 2019 conference talk. Discover how to improve images and videos, including increasing resolution and colorizing black & white footage, using advanced techniques like generative adversarial networks (GANs). Learn about faster, more efficient PyTorch-based tools developed by fast.ai, the Salk Institute, and DeOldify that can be trained in just hours on a single GPU. Delve into topics such as generative imaging, loss functions, self-attention, and reliable feature detection. Witness demonstrations of these techniques applied to various scenarios, including colorizing old movies and enhancing microscopy images. Gain insights into the challenges and solutions in brain imaging, live fluorescence imaging, and the balance between speed, quality, and data size in image processing.

New Approaches to Image and Video Reconstruction Using Deep Learning

Meta
Add to list