Welcome!

Here we list and detail out our progress, which includes but is not restricted to:

• Stanford CS231n Assignments
• Paper reviews
• Implementations
• Explorations of concepts, domains, fields, etc.

CS231n Assignments - Github

• Jaewon Chung
• Jinho Lee

Papers

• [01.05] Deformable Convolutional Networks (arXiv/review)
• [01.12] You Only Look Once: Unified, Real Time Object Detection (arXiv/review)
• [01.12] Ask Me Anything: Dynamic Memory Networks for Natural Language Processing (arXiv/review)
• [01.12] Image Super-Resolution Using Deep Convolutional Networks (arXiv/review/history)
• [01.19] XNOR-Nets: ImageNet Classification Using Binary Convolutional Neural Networks (arXiv/review)
• [01.19] Show and Tell: A Neural Image Caption Generator (arXiv/review)
• [01.19] word2vec: Efficient Estimation of Word Representations in Vector Space(arXiv/review)
• [01.26] AmbientGAN: Generative Models from Lossy Measurements (paper/review)
• [02.02] Fully Convolutional Networks for Semantic Segmentation (paper/review)
• [02.16] Progressive Growing of GANs for Improved Quality, Stability, and Variation(arXiv/review)

Concepts

• [01.05] Generative Adversarial Nets (arXiv/background/brief_review)
• [01.12] Single Image Super-Resolution (history)
• The Autoencoder Family (background knowledge/explanation)
  • [01.26] Autoencoder, Denoising Autoencoder(AE, DAE Implementation)
  • [02.02] Variational Autoencoder(VAE Implementation)
  • [02.23] Conditional Variational Autoencoder(CVAE Implementation)

Tutorials

• [01.26] Tutorial for cs231n Assignment Setup with GCP(Google Cloud Platform) (github repo)
• [01.31] Pytorch tutorial with Jupyter Notebook (github repo)
• [02.08] DCGAN tutorial (wiki page)