Deep Learning 소개 - Kangwonleeck/ML/deep_learning_intro.pdf · 2016-09-27 · Learning to Learn...

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 포착되었다.

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

Interpretable Predictive Model in Healthcare