Visualizing CNN and Neural aesthetics
Slide about "Visualizing CNNs and Neural aesthetic" topic.
This repository contains Understanding CNN by visualizing & Neural Style Transfer tutorial notebook. I further provide an overview of the current progress of NST topic, which is receiving increasing attention from both in academic literature and industrial companies.
The src/ folder contains experiment TensorFlow code for Visualizing CNN (Using Inception_V1 trained model), Fooling CNN by adversarial examples, Refining Neural image & Deep dream.
Some results from these experiments:
Neural Style transfer
Oak Tree styled by dwelling image
Ha Long Bay styled by eva image
Fooling CNN by Adversarial noise
| Predicted as Kelpie Confidence: 0.872 |
Adversarial noise | Predicted as Giant panda Confidence: 0.996 |
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Refining neural image
Tensor Flowers
Deepdream
Reference materials:
1. Stanford CS231n - Visualizing and understanding CNN
2. Yongcheng Jing et al.(2018) - Neural Style Transfer: A Review
3. Christian Richardt, Rainbow Group - Non-Photorealistic Rendering (NPR)
4. Deeplearning.ai Course 4
5. Udemy Advanced Computer Vision
6. Inceptionism: Going Deeper into Neural Networks