# Visualizing CNNs

• 관련 논문들 순서대로 총정리: https://blog.lunit.io/2017/04/09/back-projecting-mid-level-activations/

# Convolution 관련

• understanding convolutions (colah): http://colah.github.io/posts/2014-07-Understanding-Convolutions/
• colvolution-arithmetic: https://tensorflow.blog/a-guide-to-convolution-arithmetic-for-deep-learning/
• correlation vs. convolution: http://www.popit.kr/%EB%94%AE%EB%9F%AC%EB%8B%9D%EC%98%81%EC%83%81%EC%B2%98%EB%A6%AC-convolution-correlation-%EC%9D%B4%ED%95%B4%ED%95%98%EA%B8%B0/

# 각 논문을 위한 참고 링크들

# DeconvNet

• 저자 직강: https://www.youtube.com/watch?v=ghEmQSxT6tw
• 라온피플: https://laonple.blog.me/220680023908
• 간단정리: http://dambaekday.tistory.com/3
• F.T가 왜 Deconv로 동작하는지 1: http://soumith.ch/ex/pages/2014/08/07/why-rotate-weights-convolution-gradient/
• F.T가 왜 Deconv로 동작하는지 2: https://www.facebook.com/groups/TensorFlowKR/permalink/454949628179434/
• open research: http://openresearch.ai/t/zf-net-visualizing-and-understanding-convolutional-networks/30
• 구현: https://github.com/InFoCusp/tf_cnnvis
• https://www.slideshare.net/smrl7460/paper-review-visualizing-and-understanding-convolutional-networks-82147562
• http://ferguson.tistory.com/5

# Guided-Backprop

• https://medium.com/@konpat/striving-for-simplicity-the-all-convolutional-net-springenberg-et-al-2014-85da395be338

# ResNet visualization

• Visualizing Residual Networks(paper): 학생들이 프로젝트 과제로 쓴거라 별 내용은 없네...