CNN Architecture & Transfer Learning with TensorFlow 2 (Jupyter Noteook을 이용한 실습 포함)
Email: es.park@modulabs.co.kr  

 컴퓨터비젼 관련 연구를 진행하다보면 일반적으로 많이 활용하게 되는 것이 전이학습(Transfer learning) 입니다. 본 과정에서는 전이학습에 많이 사용되는 CNN 구조를 공부하면서 CNN을 더 깊게 이해하는 것을 목표로 합니다. 또한 강의의 중간 중간 TensorFlow2를 이용한 레이어 구현 및 전이학습 실습을 추가하여 배운 내용을 활용해 볼 수 있도록 구성하였습니다.

Photometric Stereo
Email: changick@gmail.com  

Image Registration Based on Conventional Features to Convolutional Features
Email: niceko@kmu.ac.kr  

  영상정합에서 전통적으로 사용되어 오던 SIFT, SURF등의 알고리즘이 최근에는 CNN을 이용한 특징 추출과 특징기술자를 기반의 방법론으로 바뀌고 있다. 본 강의에서는 전통적인 영상 정합 기술을 리뷰하고, CNN특징기술자를 바탕으로 변형된 영상간의 정합이 어떠한 방법으로 이루어 지고 있는지, 기본적인 CNN 부터 deep graphical feature learning에 걸쳐 소개하도록 한다.

  본 세미나에서는 딥러닝 기반 표정인식을 위하여, 얼굴을 검출하고, 검출된 얼굴을 정규화해서 표정인식을 위한 학습 및 테스트데이터를 만들고, 이를 바탕으로 표정인식 모델을 학습하는 과정을 OpenCV와 Python을 가지고 차례대로 진행해 봄으로써 표정인식을 위한 딥러닝 학습의 기본을 익힐 수 있도록 구성하였습니다.

Deep Learning Models for Few-shot Classification
Email: hypark@knu.ac.kr  

Data and visual recognition with and without deep learning
Email: jhc@gist.ac.kr  

 In this lecture, we show that structure learning may be carried out at different scales which will in turn, reflect the intrinsic data structure of interest for different specific applications. Given a typically limited number of degrees of freedom of any data, we propose a lower rank structure for the information space relative to its embedding space. We further argue that the self-representative nature of the data strongly suggests the flexible structure of union-of-subspaces (UoS) model, as a generalization of a linear subspace model. This proposed structure preserves the simplicity of linear subspace models, with an additional capacity of a piece-wise linear approximation of nonlinear data. We show a sufficient condition to use l1 minimization to reveal the underlying UoS structure, and further propose a bi-sparsity model (RoSure) as an effective strategy, to recover the given data characterization by the UoS model from non-conforming errors/corruptions. This structural characterization, albeit powerful for many applications, can be shown to be limited in large scale data (images) commonly shared features and for different applications. We make a case for further refinement by invoking a joint and principled scale-structure atomic characterization, which is demonstrated to improve performance. This resulting Deep Dictionary Learning approach is based on symbiotically formulating a classification problem regularized by a reconstruction problem. A theoretical rationale is also provided to contrast this work to Convolutional Neural Networks, with a demonstrably competitive performance. We also propose a novel structure of Deep Structure Learning, we refer to as Volterra Neural Networks, which do away with the non-linear transformations (ReLu, Sigmoid, etc.) and are shown with a capacity to outperform the state of the art CNN in Video Processing Problems. Substantiating examples are provided, and the application and performance of these approaches are shown for a wide range of problems such as video segmentation and object classification.

 

Bio : Hamid Krim (ahk@ncsu.edu) is presently Professor of Electrical Engineering in the ECE Department, North Carolina State University, Raleigh, leading the Vision, Information and Statistical Signal Theories and Applications Laboratory. His research interests are in statistical signal and image analysis and mathematical modeling with a keen emphasis on applied problems in classification and recognition using geometric and topological tools. His research work has been funded by many Federal and Industrial agencies, including a NSF Career award. He has served on the IEEE editorial board of SP, and the TCs of SPTM and Big Data Initiative, as well as an AE of the new IEEE Transactions on SP on Information Processing on Networks, and of the IEEE SP Magazine. He is also one of the 2015-2016 Distinguished Lecturers of the IEEE SP Society.

Attention Models and Memory Networks
Email: ijkim@handong.edu  

Supervised and semi-supervised learning for image recognition
Email: m121754@gmail.com  

여름학교 등록비
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70,000원

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