확률론 기초를 정리하고자 합니다. 참고-확률론 기초 참고-이항분포, 다항분포, 베타분포, 디리클레분포 참고-변분추론(Variational Inference) 참고-unsupervised generative models

목차

• Visualize
  • First Layer: Visualize Filters(kernels)
  • Visualizing Activations
  • Maximally Activating Patches
  • Last Layer: Nearest Neighbors
  • Last Layer: Dimensionality Reduction
  • Occlusion Experiments
  • Saliency Maps
  • Saliency Maps: Segmentation without supervision
• Generate
  • Fooling Images / Adversarial Examples
  • DeepDream: Amplify existing features
  • Feature Inversion(Reconstruct)
  • Texture
    • Texture Synthesis
    • Texture Synthesis: Nearest Neighbor
    • Neural Texture Synthesis: Gram Matrix
    • Neural Texture Synthesis
    • Neural Texture Synthesis: Texture = Artwork
  • Style
    • Neural Style Transfer: Feature + Gram Reconstruction
    • Neural Style Transfer
    • Neural Style Transfer: Multiple Style Images
    • Fast Style Transfer
    • One Network, Many Styles

표

마크다운으로 표 만들어주는 사이트(markdown table generator)

Reference

• https://junjiwon1031.github.io/2017/09/08/Single-Shot-Multibox-Detector.html
• https://sites.google.com/site/bimprinciple/in-the-news/dibleoning-euliyonghangaegchegeomchulr-cnnyolossd
• https://m.blog.naver.com/PostView.nhn?blogId=sogangori&logNo=221007697796&proxyReferer=https%3A%2F%2Fwww.google.co.kr%2F
• https://m.blog.naver.com/sogangori/221009464294