Deep Vision and Graphics
This repo supplements course "Deep Vision and Graphics" taught at YSDA @fall'21. 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 Latent models (GLO, AEs, flow models, diffusion models, VQ-VAE, generative transformers, CLIP, DALL-E)
- week10 Generative adversarial networks
- week11 Shape and motion estimation: spatial transformers, optical flow, stereo, monodepth, point cloud generation, implicit and semi-implicit shape representations
- week12 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