Deep Learning 소개
- 현재 딥러닝 기술 수준 강원대학교 IT대학
이창기
Object Recognition
https://www.youtube.com/watch?v=n5uP_LP9SmM
Semantic Segmentation
https://youtu.be/ZJMtDRbqH40
Semantic Segmentation
VGGNet + Deconvolution network
Image Completion
https://vimeo.com/38359771
Neural Art
• Artistic style transfer using CNN
Hand Writing by Machine
Input: recurrent neural network handwriting generation demo Style:
http://www.cs.toronto.edu/~graves/handwriting.html LSTM RNN:
Music Composition
https://highnoongmt.wordpress.com/2015/05/22/lisls-stis-recurrent-neural-networks-for-folk-music-generation/
Image Caption Generation
Visual Question Answering
Facebook: Visual Q&A
Play Game
Word Analogy
King – Man + Woman ≈ Queen
http://deeplearner.fz-qqq.net/
Neural Machine Translation
http://104.131.78.120/
Neural Conversation Model
Abstractive Text Summarization
로드킬로 숨진 친구의 곁을 지키는 길고양이의 모습이 포착 되었다.
RNN_search+input_feeding+CopyNet
Learning to Execute
LSTM RNN
Learning Approximate Solutions
• Travelling Salesman Problem: NP-hard
• Pointer Network can learn approximate solutions: O(n^2)
Learning to Learn with RNN
• Deep Learning: hand-designed features learned features
• This technique: hand-designed update rules learned update rules SGD:
Momentum:
Adagrad:
Adadelta or RMSprop:
Adam:
One Shot Learning
• Learning from a few examples
• Matching Nets use attention and memory
a(x1,x2) is a attention kernel
Binarized Neural Networks
BNN: neural networks with binary weights (i.e., 1 or -1) and activations at run-time 7 times faster
Binary weight filters
