ssenichev / deep_vision_and_graphics

Course about deep learning for computer vision and graphics co-developed by YSDA and Skoltech.

Deep Vision and Graphics

This repo supplements course "Deep Vision and Graphics" taught at YSDA @fall'22. The course is the successor of "Deep Learning" course taught at YSDA in 2015-2021. New course focuses more on applications of deep learning for computer vision.

Lecture and seminar materials for each week are in ./week* folders. Homeworks are in ./homework* folders.

General info

• Telegram chat room (russian).
• YSDA deadlines & admin stuff can be found at the YSDA LMS (ysda students only).
• Any technical issues, ideas, bugs in course materials, contribution ideas - add an issue

Syllabus

• week01 Intro, recap of Neural network basics, optimization, backprop, biological networks
• week02 Images, linear filtering, convolutional networks, batchnorms, augmentations
• week03 ConvNet architectures and how to find them, sparse convolutions in 3D, ConvNets for videos, transfer learning
• week04 Dense prediction: semantic segmentation, superresolution/image synthesis, perceptual losses
• week05 Non-convolutional architectures: transformers (some recap of their use in NLP), mixers, FFT convolutions
• week06 Visualizing and understanding deep architectures, adversarial examples
• week07 Object detection, instance/panoptic segmentation, 2D/3D human pose estimation
• week08 Representation learning: face recognition, verification tasks, self-supervised learning, image captioning
• week09 Generative adversarial networks
• week10 Latent models (GLO, AEs, VQ-VAE, generative transformers)
• week11 Flow models, diffusion models, generative transformers, CLIP, DALL-E
• week12 Shape and motion estimation: spatial transformers, optical flow, stereo, monodepth, point cloud generation, implicit and semi-implicit shape representations
• week13 New view synthesis: multi-plane images, neural radiance fields, mesh-based and point-based representations for NVS, neural renderers

Contributors & course staff

Course materials and teaching performed by

• Victor Lempitsky - all main track lectures
• Victor Yurchenko - seminars, homeworks, admin stuff
• Fedor Ratnikov - seminars, homeworks, admin staff
• To be continued