/papers

My implementation of chosen Deep Learning and Reinforcement Learning papers. *todo* :)

Architectures

AlexNet: https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks

Colab notebook: https://colab.research.google.com/drive/1a1b_932EMueQXTrH5LAszh0x8VdVw3M-?usp=sharing

ZFNet: https://arxiv.org/abs/1311.2901

VGG16: https://arxiv.org/abs/1505.06798

ResNet: https://arxiv.org/abs/1704.06904

GoogLeNet: https://arxiv.org/abs/1409.4842

Inception: https://arxiv.org/abs/1512.00567

Xception: https://arxiv.org/abs/1610.02357

MobileNet: https://arxiv.org/abs/1704.04861

Semantic Segmentation

FCN: https://arxiv.org/abs/1411.4038

SegNet: https://arxiv.org/abs/1511.00561

UNet: https://arxiv.org/abs/1505.04597

PSPNet: https://arxiv.org/abs/1612.01105

DeepLab: https://arxiv.org/abs/1606.00915

ICNet: https://arxiv.org/abs/1704.08545

ENet: https://arxiv.org/abs/1606.02147

Generative adversarial networks

GAN: https://arxiv.org/abs/1406.2661

DCGAN: https://arxiv.org/abs/1511.06434

WGAN: https://arxiv.org/abs/1701.07875

Pix2Pix: https://arxiv.org/abs/1611.07004

CycleGAN: https://arxiv.org/abs/1703.10593

Object detection

RCNN: https://arxiv.org/abs/1311.2524

Fast-RCNN: https://arxiv.org/abs/1504.08083

Faster-RCNN: https://arxiv.org/abs/1506.01497

Mask R-CNN: https://arxiv.org/abs/1703.06870

SSD: https://arxiv.org/abs/1512.02325

YOLO: https://arxiv.org/abs/1506.02640

YOLO9000: https://arxiv.org/abs/1612.08242

Reinforcement Learning

Playing Atari: https://arxiv.org/pdf/1312.5602v1.pdf

Other

DocParser: https://arxiv.org/abs/1911.01702 (Mask R-CNN)

Binarized Neural Networks: https://papers.nips.cc/paper/6573-binarized-neural-networks

DreamCoder: https://arxiv.org/abs/2006.08381

Physics-based Human Motion Estimation and Synthesis from Videos: https://arxiv.org/abs/2109.09913

Practical Reinforcement Learning For MPC: https://arxiv.org/abs/2003.03200