### Category: Notes

14 Posts

Source Paper: [ICCV'2017] https://arxiv.org/abs/1703.06868 Authors: Xun Huang, Serge Belongie Code: https://github.com/xunhuang1995/AdaIN-style Contributions In this paper, the authors present a simple yet effective approach that for the first time enables arbitrary style transfer in real-time. Arbitrary style transfer: takes a content image $C$ and an arbitrary style image $S$ as inputs, and synthesizes an output image with the same content as $C$ and the same syle as $S$. Background Batch Normalization Given a input batch $x \in \mathbb{R}^{N \times C \times H \times W}$, batch normalization (BN) normalizes the mean and standard deviation for each individual feature channel: $$\mathrm{BN}(x)=\gamma\left(\frac{x-\mu(x)}{\sigma(x)}\right)+\beta$$ where $\gamma , \beta \in \mathbb{R}^{C}$ are affine parameters learned from data. $\mu(x) , \sigma(x) \in \mathbb{R}^{C}$ are mean and standard deviation computed across batch size and spatial dimensions, independently. $$\mu_{c}(x)=\frac{1}{N H W} \sum_{n=1}^{N} \sum_{h=1}^{H} \sum_{w=1}^{W} x_{n c h w}$$ $$\sigma_{c}(x)=\sqrt{\frac{1}{N H W} \sum_{n=1}^{N} \sum_{h=1}^{H} \sum_{w=1}^{W}\left(x_{n c h w}-\mu_{c}(x)\right)^{2}+\epsilon}$$ Instance Normalization Original feed-forward stylization method  utilizes BN layers after the convolutional layer. Ulyanov et al.  found using Instance Normalization…
Source Authors: Huan Wang, Yijun Li, Yuehai Wang, Haoji Hu, Ming-Hsuan YangPaper: [CVPR2020] https://arxiv.org/abs/2003.08436Code: https://github.com/mingsun-tse/collaborative-distillation Contributions It proposes a new knowledge distillation method "Collobrative Distillation" based on the exclusive collaborative relation between the encoder and its decoder. It proposes to restrict the students to learn linear embedding of the teacher's outputs, which boosts its learning. Experimetenal works are done with different stylization frameworks, like WCT and AdaIN. Related Works Style Transfer WCT: Li, Y., Fang, C., Yang, J., Wang, Z., Lu, X., & Yang, M. H. (2017). Universal style transfer via feature transforms. arXiv preprint arXiv:1705.08086.AdaIN: Huang, X., & Belongie, S. (2017). Arbitrary style transfer in real-time with adaptive instance normalization. In Proceedings of the IEEE International Conference on Computer Vision (pp. 1501-1510). Model Compression low-rank decomposition pruning quantization knowledge distillationKnowledge distillation is a promising model compression method by transferring the knowledge of large networks (called teacher) to small networks (called student), where the knowledge can be softened probability (which can reflect the inherent class similarity structure known as dark knowledge) or sample relations (which…
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Scaled-YOLOv4: Scaling Cross Stage Partial Network In this reading notes: We have reviewed some basic model scaling method: width, depth, resolution, compound scaling. We have computed the operation amount of residual blocks, and showed the relation with input image size (square), number of layers (linear), number of filters (square). We have presented the proposed Cross-Stage Partial (CSP) method that decreases the operations and improves the performance of basic CNN layers. PPT can be download from: https://connectpolyu-my.sharepoint.com/:p:/g/personal/18048204r_connect_polyu_hk/ET9zlHku9TFApqdl1A5NTV8BjFXPLizhCMupm6Ohcbehig?e=hhLlyc This is an embedded Microsoft Office presentation, powered by Office.