cs231n_deep_learning_for_computer_vision学习笔记
Lecture 4: Neural Networks and Backpropagation
Multi-layer Perceptron(多层感知机)
Backpropagation(反向传播)
Backprop Review Session(反向传播复习课)
Lecture 6: CNN Architectures
Batch Normalization
Transfer learning
AlexNet, VGG, GoogLeNet, ResNet
Lecture 7: Training Neural Networks
Activation functions
Data processing
Weight initialization
Hyperparameter tuning
Data augmentation
Lecture 8: Visualizing and Understanding
Feature visualization and inversion
Adversarial examples
DeepDream and style transfer
PyTorch Review Session
Lecture 9: Object Detection and Image Segmentation
Single-stage detectors
Two-stage detectors
Semantic/Instance/Panoptic segmentation
Lecture 10: Recurrent Neural Networks
RNN, LSTM, GRU
Language modeling
Image captioning
Sequence-to-sequence
Object Detection & RNNs Review Session
Lecture 11: Attention and Transformers
Self-Attention
Transformers
Lecture 12: Video Understanding
Video classification
3D CNNs
Two-stream networks
Multimodal video understanding
Midterm Review Session
In-Class Midterm
四、Reconstructing and Interacting with the Visual World
Lecture 13: Generative Models
Supervised vs. Unsupervised learning
Pixel RNN, Pixel CNN
Variational Autoencoders
Generative Adversarial Networks
Lecture 14: Self-supervised Learning
Pretext tasks
Contrastive learning
Multisensory supervision
Lecture 15: Low-Level Vision(Guest Lecture by Prof. Jia Deng from Princeton University)
Optical flow
Depth estimation
Stereo vision
Lecture 16: 3D Vision
3D shape representations
Shape reconstruction
Neural implicit representations
五、Human-Centered Applications and Implications
Lecture 17: Human-Centered Artificial Intelligence
AI & healthcare
Lecture 18: Fairness in Visual Recognition(Guest Lecture by Prof. Olga Russakovsky from Princeton University)
Final Project Poster Session
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