2016年11月11日ICFHR-参会报告

The challenge is to design systems that handle unseen problems
Part of deep-learning success may be just caused by the amount of data: better comparative evaluation is needed Iterative recognition and ranking works great!
THE MONK SYSTEM
Large-scale processing of handwritten historical documents. Monk e-science web service addressing these questions: What ? Word retrieval by 24/7 machine learning
A European Google for handwriting
When ? Medieval manuscript dating Where? Geographical localization
Goa l: uploading of charters from 1300-1550 on a server
Engineered neural-network architectures vs Engineered features: we’re still not there, either way!
2.Handwriting and Speech Recognition: From Bayes Decision Rule to Deep Neural Networks.
Sequence-to-Sequence Conversion and Recognition: Human Language Technology (HLT)
characteristic properties: well-defined ’classification’ tasks: due to 5000-year history of (written!) language well-defined goal: letters or words (= full forms) of the language easy task for humans (in native language!) （人类很容易处理） hard task for computers（电脑很难） (as the last 50 years have shown!) unifying view（统一意见）: formal task: input sequence → output sequence output sequence: sequence of words/letters in a natural language（自然语言中的文字、字 母序列） models of context and dependencies（模型的上下文依赖）: within input and output sequences across input and output sequence
Who? Writer identification
WHAT ?： RECOGNITION AND RETRIEVAL OF TEXT
WHEN ? (HAS IT BEEN WRITTEN)
WHO?
CONCLUSIONS
Deep learning is a powerful concept But it is not enough, for building autonomous and intelligent agents
(IWFHR).
The most important event in the field of handwriting recognition.
会议旨在： Bring together international experts from academia and industry to share their experiences. To promote research and development in all aspects of handwriting recognition and applications.
Deep Learning is no computational intelligence, yet Intelligence by proxy: over the supervised labels
A smart human PhD is always necessary
No general intelligence: each experiment is a one-trick pony Extensive, laboratory-based training
Google self-driving cars Predicting internet user interests (‘cookies’)
Twitter-based epidemiology (‘flu tweets’)
Create a van Gogh or Munch version of a photograph
Coloring of B/W movies
Learning to play Atari Breakout, Pacman etc.
AlphaGO: computer wins at playing GO
Improved training (loss function, softmax, ReLU) With 1000 hidden layers (Susillo & Abbot, 2015) etc.
语音识别和手写识别的相似性
手写识别
PREPROCESSING: DESLANTING
Reduce vertical distortions through shearing angle normalization
FEATURE EXTRACTION
Shift (overlapping) sliding window from left to right over the image
Prof. Hermann Ney (,德国亚琛工业大) 3. Online handwriting recognition: past, present and future. Prof. Masaki Nakagawa (日本东京农工大学 )
1.HOW DEEP IS DEEP & WHAT'S NEXT IN COMPUTATIONAL INTELLIGENCE
部分手写识别的论文介绍
ICFHR2016 简单介绍
ICFHR全称： International Conference on Frontiers in Handwriting Recognition formerly called International Workshop on Frontiers of Handwriting Recognition
Recent advances in ML Neural network & Deep Learning Critical remarks Monk: massive shallow but convenient learning
DEEP LEARNING/RECENT ADVANCES IN ML
3 个Keynote How deep is deep & what's next in computational intelligence? Handwriting and Speech Recognition: From Bayes Decision Rule to Deep Neural Networks. Online handwriting recognition: past, present and future. 7个 Oral session 2个 Poster session Panel session New Frontiers in Handwriting Recognition.
Generalisation to real, new data from new sensors, from new contexts is still difficult: k-fold evaluation is still a scam:
i.i.d.’ and sampled from one cleaned pool of data yields overly optimistic performance estimates
ICFHR2016参会报告
报告Байду номын сангаас：刘吉
目录
ICFHR2016 简单介绍
ICFHR keynote
How deep is deep & what's next in computational intelligence? Handwriting and Speech Recognition: From Bayes Decision Rule to Deep Neural Networks. Online handwriting recognition: past, present and future.
bringtogetherinternationalexpertsfromacademiapromoteresearchallaspectshandwritingrecognitionapplicationsicfhr2016简单介绍icfhr2016由自动化所和清华大学深圳研究院联合举办icfhr2016简单介绍会议流程简介会议注册接待会议开幕要点keynote口头报告oral海报poster宴会讨论panel比赛颁奖闭幕icfhr2016简单介绍个keynotehowdeepwhatsnextcomputationalintelligence
HISTORY OF NN’S
1957 - 1st generation (Rosenblatt’s Perceptron) 1983 – 2nd generation (Werbos/Rumelhart)
1996 – NN – winter
2000 – 3rd generation: Deep Learning (Hinton//Lecun) Computer vision Speech/handwriting: sequence classification LSTM/BLSTM (Schmidhuber/Liwicki/Grav） Remark: handwriting recognition played an role. Early 2D convolutional nets by LeCun:IWFHR 1990, Cenparmi, Montreal
WINDOW-BASED TRANSFORMATIONS
DEFINE ‘DEEP’!
Is it the convolutional aspect?
Is it the number of layers?
Is it the dimensionality reduction?
TIME TO IDENTIFY WHAT CANNOT BE DONE!
ICFHR KEYNOTE
1.How deep is deep & what's next in computational intelligence?
Prof. Lambert Schomaker (荷兰格罗宁根大学)
2.Handwriting and Speech Recognition: From Bayes Decision Rule to Deep Neural Networks.
ICFHR2016 简单介绍
ICFHR2016由自动化所和清华大学深圳研究院联合举办
ICFHR2016 简单介绍
会议流程简介 会议注册、接待 会议开幕 要点(Keynote） 口头报告(Oral) 海报(Poster) 宴会 讨论(Panel) 比赛颁奖 闭幕
ICFHR2016 简单介绍