The content discusses the U-Net algorithm architecture, which consists of a contracting path for understanding the overall image and an expansive path for precise detail extraction. The design allows the model to train well with limited image data and includes techniques to enhance image processing capabilities. The contracting path involves convolution and max-pooling layers for feature extraction, while the expansive path includes upsampling and convolution layers for spatial restoration. The final layer uses a 1×1 convolution to map feature vectors to the desired classes for pixel-level classification. Data augmentation techniques are used during training to enhance deformation invariance, and random deformation grids are employed for robustness. Weighted loss functions are used to segment touching objects of the same class. U-Net in Stable-diffusion models is applied for capturing and enhancing details and multi-scale feature fusion. The skip connections in U-Net help maintain and enhance image details, while the encoder-decoder design allows for multi-scale feature fusion to control local feature details accurately. References to the original paper, PyTorch implementation, and applications in AI drawing are provided for further reading.
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Source link: https://medium.com/@renhehuang0723/ai%E5%BD%B1%E5%83%8F%E8%AB%96%E6%96%87-03-u-net-%E8%AB%96%E6%96%87%E7%AD%86%E8%A8%98%E6%95%B4%E7%90%86-302654bd8ec6?source=rss——stable_diffusion-5
U-Net algorithm structure: recent paper notes organization and understanding. #AIresearch
