Course Development and Instructor - Computer Vision Discussion Section

In spring 2022, I designed and was the instructor for the first iteration of ‘Computer Vision - paper discussion section’ at Indiana University. The course was designed to explore the seminal papers on different architectures of deep-learning and computer vision. The course traced the history of development of Convolutional Neural Networks (like, LeNet, AlexNet, GoogLeNet, ResNets, Wide ResNet, Stochastic ResNet, ResNeXt, DenseNet, ConvMixer, Xception Net, etc.), Multi-layer Perceptron based networks (like, MLP Mixer, ResMLP, cycleMLP and S^2 MLP model), Transformer based Networks for vision application like, Vision Transformer, DeiT, Swin Transformer, Local ViT, CvT etc. papers) and other important architectures like SAN (self-attention Network), shift-based papers etc. The class met weekly with more than 130 students taking part in the discussion. Check the discussion schedule and slides for more details.

.