Course Development and Instructor - Computer Vision Discussion Section

In spring 2024 and 2025, I taught the third and fourth iteration of the ‘Computer Vision - discussion section’ at Indiana University. I discuss a list of seminal papers exploring deep learning (DL) architectures. I designed the first iteration of the DL discussion section in Spring 2022, it is designed to explore the seminal papers exploring DL architectures to develop an intuitive as well as mathematical understanding of major concepts in DL. In 16 weeks, the course roughly covers major architectures in three broad sections, Convolution-based networks (CNNs), Multi-layer perceptron-based networks (MLPs), and Transformer-based networks (ViTs). In CNNs, we cover models like LeNet, AlexNet, GoogLeNet, ResNets, Wide ResNet, Stochastic ResNet, ResNeXt, DenseNet, ConvMixer, and Xception Net. In MLPs, we cover models like MLP Mixer, ResMLP, cycleMLP, and S^2 MLP model, and in ViTs, we cover models like Vision Transformer, DeiT, Swin Transformer, Local ViT, Convolutional-ViT, etc. We also explore shift operation-based networks. The class meets weekly with more than 150 students participating in the discussion. The list of papers covered in the discussion is below

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