https://github.com/popcornell/keras-triplet-center-loss

Simple Keras implementation of Triplet-Center Loss on the MNIST dataset
https://github.com/popcornell/keras-triplet-center-loss

center-loss keras machine-learning mnist tensorflow triplet-loss

Last synced: 23 days ago
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Simple Keras implementation of Triplet-Center Loss on the MNIST dataset

• Host: GitHub
• URL: https://github.com/popcornell/keras-triplet-center-loss
• Owner: popcornell
• License: gpl-3.0
• Created: 2019-05-28T08:51:31.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2019-07-03T17:45:08.000Z (over 5 years ago)
• Last Synced: 2023-03-06T02:39:02.519Z (over 1 year ago)
• Topics: center-loss, keras, machine-learning, mnist, tensorflow, triplet-loss
• Language: Python
• Homepage:
• Size: 4.34 MB
• Stars: 42
• Watchers: 5
• Forks: 11
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# keras-triplet-center-loss

A simple Keras implementation of Triplet-Center Loss on the MNIST dataset. 

As a reference in this repository also implementations of other two similar losses, 

Center-Loss and Triplet-Loss are included. 

The Center-Loss implementation is from **shamangary**: https://github.com/shamangary/Keras-MNIST-center-loss-with-visualization

 

The Triplet-Loss implementation is from  **KinWaiCheuk**: https://github.com/KinWaiCheuk/Triplet-net-keras

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### Triplet-Center Loss

Triplet-Center Loss has been introduced by He et al. in https://arxiv.org/abs/1803.06189. 

It is an "hybrid" loss between Center Loss and Triplet Loss that allows to maximise inter-class distance and 

minimize intra-class distance.

### Details 

In this repository a simple implementation on the MNSIT or alternatively Fashion MNIST is 

shown. 

Running **main.py** will start sequentially 4 training routines with 4 different losses:

* Categorical Crossentropy only 

* Center-loss + Categorical Crossentropy 

* Triplet-loss + Categorical Crossentropy 

* Triplet-Center loss + Categorical Crossentropy 

In Folder **runs** there will be the results of those models, including Tensorboard summaries. 

Also T-SNE is run on the embeddings to visualize how the network internal representation changes as the loss is changed.

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##### triplet-center loss, T-SNE on internal representation (Train Data):

![Image of Triplet_Center_Loss](https://github.com/popcornell/keras-triplet-center-loss/blob/master/runs/triplet_center_loss/Samples%20from%20Train%20Data%2C%20triplet_center_loss.png)

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##### Center loss, T-SNE on internal representation (Train Data):

![Image of _Center_Loss](https://github.com/popcornell/keras-triplet-center-loss/blob/master/runs/center_loss/Samples%20from%20Train%20Data%2C%20center_loss.png)

----

##### Triplet loss, T-SNE on internal representation (Train Data):

![Image of _Triplet_Loss](https://github.com/popcornell/keras-triplet-center-loss/blob/master/runs/triplet_loss/Samples%20from%20Train%20Data%2C%20triplet_loss.png)

As it can be seen the triplet-center loss maximises the inter-class distance as the Triplet Loss while keeping the Center-loss characteristic of minimizing intra-class distance. 

Another advantage of Triplet-Center loss is that it does not need advanced batching and triplet selection mining techniques as the Triplet-Loss does.