https://github.com/hunkim/deep_architecture_genealogy

Deep Learning Architecture Genealogy Project
https://github.com/hunkim/deep_architecture_genealogy

Last synced: 7 months ago
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Deep Learning Architecture Genealogy Project

• Host: GitHub
• URL: https://github.com/hunkim/deep_architecture_genealogy
• Owner: hunkim
• Created: 2017-11-04T22:04:20.000Z (about 8 years ago)
• Default Branch: master
• Last Pushed: 2021-02-14T04:24:09.000Z (almost 5 years ago)
• Last Synced: 2025-04-09T10:04:15.485Z (8 months ago)
• Language: Python
• Homepage: https://coggle.it/diagram/Wf5mYoJbsgABUF9P
• Size: 1.4 MB
• Stars: 1,218
• Watchers: 110
• Forks: 194
• Open Issues: 6
• Metadata Files:
  • Readme: README.md

Awesome Lists containing this project

• Best-Audio-Classification-Resources-with-Deep-learning - Deep architecture genealogy - Genealogy of DL architectures (Other useful related lists and resources)
• awesome-deep-learning-music - Deep architecture genealogy - Genealogy of DL architectures (Other useful related lists and resources)

README

          
# Deep Architecture Genealogy

There are so many new models and architectures. If you find something interesting and worth paying attention to, please send us a pull requests (PR) and write issues.

`README.md` is automatically generated. Please send PRs on the `Neural Net Arch Genealogy.txt` file.

## Mindmap Coggle Link

https://coggle.it/diagram/Wf5mYoJbsgABUF9P

![https://coggle.it/diagram/Wf5mYoJbsgABUF9P](Neural_Net_Arch_Genealogy.png)

## Text Version

This is automatically generated. Please send a PR on the `Neural Net Arch Genealogy.txt` file.

* Reinforcement Learning Algorithms

  * [A3C, '16.02.06](https://arxiv.org/abs/1602.01783)

  * [DARLA, '17.07.26](https://arxiv.org/pdf/1707.08475.pdf)

  * [ACTKR, '17.08.17](https://arxiv.org/pdf/1708.05144.pdf)

  * [c51, '17.10.27](https://arxiv.org/pdf/1710.10044.pdf)

* CNN

  * [AlexNet, '12.12](https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf)

  * [VggNet, '14.09](https://arxiv.org/pdf/1409.1556.pdf)

  * [GoogLeNet, '14.09](https://arxiv.org/abs/1409.4842)

  * [ResNet, '15.12](https://arxiv.org/pdf/1512.03385v1.pdf)

  * [DenseNet, '16.08](https://arxiv.org/pdf/1608.06993.pdf)

  * [SENet: Squeeze-and-Excitation Networks, '17.09](https://arxiv.org/abs/1709.01507)

  * Object Detection

    * [R-CNN](https://arxiv.org/pdf/1311.2524.pdf)

    * [Fast R-CNN](https://arxiv.org/pdf/1504.08083.pdf)

    * [Faster R-CNN](https://arxiv.org/pdf/1506.01497.pdf)

    * [Mask R-CNN](https://arxiv.org/pdf/1703.06870.pdf)

    * [YOLO](https://arxiv.org/pdf/1506.02640.pdf)

    * [SSD](https://arxiv.org/pdf/1512.02325.pdf)

    * [R-FCN](https://arxiv.org/pdf/1605.06409.pdf)

  * Semantic Segmentation

    * [FCN](https://arxiv.org/pdf/1411.4038.pdf)

    * [DeconvNet](https://arxiv.org/pdf/1505.04366.pdf)

    * [DeepLab](https://arxiv.org/pdf/1606.00915.pdf)

    * [U-Net](https://arxiv.org/pdf/1505.04597.pdf)

  * Super-resolution

    * [MemNet](https://arxiv.org/abs/1708.02209)

    * [FSRCNN](https://arxiv.org/1608.00367)

    * [SRCNN](https://arxiv.org/abs/1501.00092)

    * [VDSR](https://arxiv.org/abs/1511.04587)

    * [DRCN](https://arxiv.org/abs/1511.04491)

    * [LabSRN](https://arxiv.org/abs/1704.03915)

    * [EDSR](https://arxiv.org/abs/1707.02921)

  * TTS

    * [Wavenet, '16.09.12](https://arxiv.org/abs/1609.03499)

* Generative Models

  * Autoregressive models

    * [MADE, '15.02.12](https://arxiv.org/pdf/1502.03509.pdf)

    * [PixelRNN, '16.01.25](https://arxiv.org/pdf/1601.06759.pdf)

    * [NADE, '16.05.07](https://arxiv.org/pdf/1605.02226.pdf)

    * [PixelCNN, '16.06.16](https://arxiv.org/pdf/1606.05328.pdf)

    * [PixelCNN++, '17.01.19](https://arxiv.org/pdf/1701.05517.pdf)

  * Latent variable models

    * [VAE, '13.12.20](https://arxiv.org/pdf/1312.6114.pdf)

      * [CVAE, '14.06.20](https://arxiv.org/pdf/1406.5298.pdf)

      * [AAE, '15.11.18](https://arxiv.org/pdf/1511.05644.pdf)

      * [AVB, '17.01.17](https://arxiv.org/pdf/1701.04722.pdf)

      * [VQ-VAE, '17.11.2](https://arxiv.org/abs/1711.00937)

    * [GAN, '14.06.10](https://arxiv.org/pdf/1406.2661.pdf)

      * Variants

        * [CGAN, '14.11.06](https://arxiv.org/pdf/1411.1784.pdf)

        * [DCGAN, '15.11.19](https://arxiv.org/pdf/1511.06434.pdf)

        * [infoGAN, '16.06.12](https://arxiv.org/pdf/1704.00028.pdf)

        * [EBGAN, '16.09.11](https://arxiv.org/pdf/1609.03126.pdf)

        * [ACGAN, '16.10.30](https://arxiv.org/pdf/1610.09585.pdf)

        * [WGAN, '17.01.26](https://arxiv.org/pdf/1701.07875.pdf)

        * [BEGAN, '17.02.27](https://arxiv.org/pdf/1702.08431.pdf)

        * [WGAN-GP, '17.03.31](https://arxiv.org/pdf/1704.00028.pdf)

        * [TripleGAN, '17.03.07](https://arxiv.org/pdf/1703.02291.pdf)

      * Applications

        * [Pix2Pix, '16.11.21](https://arxiv.org/pdf/1611.07004v1.pdf)

        * [PPGN, '16.11.30](https://arxiv.org/pdf/1612.00005.pdf)

        * [StackGAN, '16.12.10](https://arxiv.org/pdf/1612.03242.pdf)

* RNN

  * [LSTM, '97.11](http://www.mitpressjournals.org/doi/10.1162/neco.1997.9.8.1735)

  * [GRU, 14.11](https://arxiv.org/abs/1412.3555)

  * [ACT: Adaptive Computation Time, '17.05](https://arxiv.org/abs/1603.08983)

  * [S2S: RNN Encoder-Decoder, '14.06](https://arxiv.org/abs/1406.1078)

    * [Attention: Jointly Learning to Align, '14.09](https://arxiv.org/abs/1409.0473)

      * [Effective Approaches to Attention, Luong et al. '15.08](https://arxiv.org/abs/1508.04025)

      * [DCN: Dynamic Coattention Networks, '16.08](https://arxiv.org/abs/1611.01604), [DCN+, '17.08](https://arxiv.org/abs/1711.00106)

      * [Transformer: Attention Is All You Need, '17.06](https://arxiv.org/abs/1706.03762)

* [Capsule Net, '17.10](https://arxiv.org/abs/1710.09829)

* Memory Networks

  * Neural Programming

    * [Neural Turing Machine,'14.10](https://arxiv.org/pdf/1410.5401.pdf)

    * [Neural Random-Access Machines,'16.02](https://arxiv.org/pdf/1511.06392.pdf)

    * [Hierarchical Attentive Memory, '16.02](https://arxiv.org/abs/1602.03218)

    * [Neural GPUs Learn Algorithms, '16.03](https://arxiv.org/pdf/1511.08228.pdf)

    * [Neural Programmer,'16.08](https://arxiv.org/pdf/1511.04834.pdf)

    * [Neural Module Networks, '16.06](https://www.cv-foundation.org/openaccess/content_cvpr_2016/html/Andreas_Neural_Module_Networks_CVPR_2016_paper.html)

    * [Hybrid Computing, '16.10](https://www.nature.com/nature/journal/v538/n7626/full/nature20101.html)

  * [Memory Networks,'14.10](https://arxiv.org/pdf/1410.3916.pdf)

  * [End-to-End Memory Network,'15.03](https://arxiv.org/pdf/1503.08895.pdf)

  * [DMN: Dynamic Memory Network, '16.03](https://arxiv.org/pdf/1506.07285.pdf), [DMN+, '16.04 ](https://arxiv.org/pdf/1603.01417.pdf) 

## Contributions

Your pull requests and issues are always welcome.