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# System prepended metadata

title: '(7/22)Computer Vision Recent Paper:ArcNet'
tags: [paper]

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# (7/22)Computer Vision Recent Paper:ArcNet 
###### tags: `paper`
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## Before Meeting
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### Author
- Jiankang Deng 
    - ![](https://i.imgur.com/LCzzRvz.png)
    - [refer](https://scholar.google.com/citations?user=Z_UoQFsAAAAJ)
- Stefanos Zafeiriou
    - ![](https://i.imgur.com/6Z087DW.png)
    - [refer](https://scholar.google.com/citations?user=QKOH5iYAAAAJ&hl=en)
- [refer]()
- [refer](https://blog.csdn.net/u014380165/article/details/80645489)
- [refer]()

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[refer](https://arxiv.org/pdf/1801.07698.pdf)
[refer]()
[refer]()


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## Recent Paper

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### ArcFace: Additive Angular Margin Loss for Deep Face Recognition

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#### Abstracion
- Deep Convolutional Neural Networks (DCNNs)
- Centre loss penalises the distance between the deep features and their corresponding class centres in the Euclidean space to achieve intra-class compactness.
- Additive Angular Margin Loss (ArcFace) to obtain highly discriminative features for face recognition.
- We show that ArcFace consistently outperforms the state-of-the-art and can be easily implemented with negligible computational overhead. 
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#### Detail
- Introduction
    - ![](https://i.imgur.com/nIiWdOW.png)

    - There are two main lines of research to train DCNNs for face recognition. 
        - train a multi-class classifier which can separate different identities in the training set, such by using a softmax classifier
        - learn directly an embedding, such as the triplet loss
        - Additive Angular Margin Loss (ArcFace) to further improve the discriminative power of the face recognition model and to stabilise the training process
        - ![](https://i.imgur.com/WjAPuaH.png)

- Proposed Approach
    - ArcFace
    - Comparison with SphereFace and CosFace
    - Comparison with Other Losses
- Experiments
    - Implementation Details
    - Ablation Study on Losses
    - Evaluation Results

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#### Conclusion
- ![](https://i.imgur.com/5mPVLKD.png)

- we proposed an Additive Angular Margin Loss function, which can effectively enhance the discriminative power of feature embeddings learned via DCNNs for face recognition
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[refer]()

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#### Abstracion
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:::info
#### Detail


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#### Conclusion

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[refer]()

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