https://github.com/danibcorr/papers-with-code

📜 Machine Learning & Deep Learning Paper Implementations: Turning Research into Code
https://github.com/danibcorr/papers-with-code

deep-learning keras open-source papers papers-collection papers-with-code python tensorflow

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📜 Machine Learning & Deep Learning Paper Implementations: Turning Research into Code

• Host: GitHub
• URL: https://github.com/danibcorr/papers-with-code
• Owner: danibcorr
• License: mit
• Created: 2024-06-28T13:22:45.000Z (6 months ago)
• Default Branch: main
• Last Pushed: 2024-09-06T15:54:08.000Z (4 months ago)
• Last Synced: 2024-09-06T18:56:39.071Z (4 months ago)
• Topics: deep-learning, keras, open-source, papers, papers-collection, papers-with-code, python, tensorflow
• Language: Python
• Homepage:
• Size: 53.7 KB
• Stars: 0
• Watchers: 2
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# 🚀 Papers with Code

Implementation of scientific papers on deep learning, mostly developed using Tensorflow.

## 📄 Index

### 👀 Computer Vision

| Paper                                                                 | Code                                |

|-----------------------------------------------------------------------|---------------------------------------|

| [Truly Shift-Invariant Convolutional Neural Networks](https://arxiv.org/pdf/2011.14214) | [APS.py](cv/APS.py)                   |

| [EfficientDet: Scalable and Efficient Object Detection](https://arxiv.org/pdf/1911.09070) | [BiFPN.py](cv/BiFPN.py)               |

| [Making Convolutional Networks Shift-Invariant Again](https://arxiv.org/pdf/1904.11486) | [BlurPool.py](cv/BlurPool.py)         |

| [CBAM: Convolutional Block Attention Module](https://arxiv.org/pdf/1807.06521) | [CBAM.py](cv/CBAM.py)                 |

| [A ConvNet for the 2020s](https://arxiv.org/pdf/2201.03545)          | [ConvNextResidualBlock.py](cv/ConvNextResidualBlock.py) |

| [All the Attention You Need: Global-Local, Spatial-Channel Attention for Image Retrieval](https://arxiv.org/pdf/2107.08000) | [GLAM.py](cv/GLAM.py)                 |

| [Going Deeper with Convolutions](https://arxiv.org/pdf/1409.4842)    | [googlelenet.py](cv/googlelenet.py)   |

| [Gradient-Based Learning Applied to Document Recognition](http://yann.lecun.com/exdb/publis/pdf/lecun-01a.pdf) | [lenet.py](cv/lenet.py)               |

| [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385) | [resnet.py](cv/resnet.py)             |

| [Squeeze-and-Excitation Networks](https://arxiv.org/pdf/1709.01507)  | [SE.py](cv/SE.py)                     |

| [Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition](https://arxiv.org/pdf/1406.4729) | [SPP.py](cv/SPP.py)                   |

| [CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features](https://arxiv.org/pdf/1905.04899) | [CutMix.py](cv/CutMix.py)                   |

| [mixup: BEYOND EMPIRICAL RISK MINIMIZATION](https://arxiv.org/pdf/1710.09412) | [MixUp.py](cv/MixUp.py)                   |

### 📉 Loss functions

| Paper                                                                 | Code                                |

|-----------------------------------------------------------------------|---------------------------------------|

| [Contractive Auto-Encoders: Explicit Invariance During Feature Extraction](http://www.icml-2011.org/papers/455_icmlpaper.pdf) | [ContractiveLoss.py](losses/ContractiveLoss.py)                   |

| [Supervised Contrastive Learning](https://arxiv.org/pdf/2004.11362) | [SupervisedContrastiveLoss.py](losses/SupervisedContrastiveLoss.py)               |

### 🪛 Optimizers

| Paper                                                                 | Code                                |

|-----------------------------------------------------------------------|---------------------------------------|

| [AUTOCLIP: ADAPTIVE GRADIENT CLIPPING FOR SOURCE SEPARATION NETWORKS](https://arxiv.org/pdf/2007.14469) | [AGC.py](optimizers/AGC.py)                   |

| [Gradient Centralization: A New Optimization Technique for Deep Neural Networks](https://arxiv.org/pdf/2004.01461) | [GCAdamW.py](optimizers/GCAdamW.py)               |

### ➿ Learning rate schedulers

| Paper                                                                 | Code                                |

|-----------------------------------------------------------------------|---------------------------------------|

| [SGDR: STOCHASTIC GRADIENT DESCENT WITH WARM RESTARTS](https://arxiv.org/pdf/1608.03983) | [WarmUpCosine.py](schedulers/WarmUpCosine.py)                   |