서버 사용과 과제 제출 안내

Lab01 - Introduction & PyTorch

Lab02 - PyTorch implementation of Linear regression / Logistic regression / MLP

Lab03 - PyTorch implementation for CNNs

조교: 김진성 [email protected]
(ICLR 2015) Very Deep Convolutional Networks for Large-Scale Image Recognition (https://arxiv.org/pdf/1409.1556.pdf)
(CVPR 2016) Deep Residual Learning for Image Recognition (https://arxiv.org/pdf/1512.03385.pdf)

Lab04 - CNN limitation (STN)

조교: 강정운 [email protected]
(NeurIPS 2015) Spatial Transformer Networks (https://arxiv.org/pdf/1506.02025.pdf)

Lab05 - Low-level vision (Single image super-resolution)

Lab06 - High-level vision & Fine-tuning (Semantic segmentaion)

조교: 강정운 [email protected]
(CVPR 2015)**** Fully Convolutional Networks for Semantic Segmentation (https://arxiv.org/pdf/1411.4038.pdf)
(ICLR 2016)**** Multi-Scale Context Aggregation by Dilated Convolutions (https://arxiv.org/pdf/1511.07122.pdf)

Lab07 - High-level vision (Object detection)

조교: 김진성 [email protected]
(ICCV 2017) Focal Loss for Dense Object Detection (https://arxiv.org/abs/1708.02002)

Lab08 - CNN Interpretation & Attention (CAM, GradCAM)

조교: 강정운 [email protected]
(CVPR 2016) Learning Deep Features for Discriminative Localization (https:// arxiv.org/pdf/1512.04150.pdf)
(ICCV 2017) Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization (https://arxiv.org/pdf/1610.02391.pdf)

Lab09 - Quantization (revised)

조교: 김진성 [email protected]
(ECCV 2016)**** XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks (https://arxiv.org/pdf/1603.05279.pdf)
(CVPR 2019) Learning to Quantize Deep Networks by Optimizing Quantization Intervals with Task Loss (https://arxiv.org/pdf/1808.05779.pdf)

Lab10 - Graphics (Style transfer) (revised)

조교: 강정운 [email protected]
(CVPR 2016) Image Style Transfer Using Convolutional Neural Networks (https://www.cv-foundation.org/openaccess/content\_cvpr\_2016/papers/Gatys\_Image\_Style\_Transfer\_CVPR\_2016\_paper.pdf)
(ECCV 2016)**** Perceptual Losses for Real-Time Style Transfer and Super-Resolution (https://arxiv.org/pdf/1603.08155.pdf)

Lab11 - GAN (Simple model, DCGAN)

조교: 김진성 [email protected]
(NeurIPS 2014) Generative Adversarial Nets (https://papers.nips.cc/paper/5423-generative-adversarial-nets.pdf)
(ICLR 2016) Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks (https://arxiv.org/pdf/1511.06434.pdf)

Lab12 - Image translation (Im2Im, CycleGAN)

조교: 강정운 [email protected]
(CVPR 2017) Image-to-Image Translation with Conditional Adversarial Networks (https://arxiv.org/pdf/1611.07004.pdf)
(ICCV 2017) Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks (https://arxiv.org/pdf/1703.10593.pdf)

Lab13 - RNN (Vanilla RNN, GRU, LSTM), Sequence2sequence model (revised)