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ConvTranspose2d

时间: 2023-10-30 17:29:53 浏览: 104

nn.ConvTranspose2d 逆卷积反卷积

本文转摘于如下链接：逆卷积的详细解释ConvTranspose2d（fractionally-strided convolutions) https://www.cnblogs.com/wanghui-garcia/p/10791328.html pytorch官方手册：https://pytorch.org/docs/stable/nn.html?highlight=convtranspose#torch.nn.ConvTranspose2d ConvTranspose2d torch.nn.ConvTranspose2d(in_channels, out_channels, kernel

ConvTranspose2d is a type of layer in deep learning used for upsampling or deconvolution of a feature map. It is also known as a transposed convolutional layer. It is often used in image processing tasks like image segmentation, object detection, and image generation. ConvTranspose2d works by taking a low-resolution feature map and expanding it into a higher resolution feature map. This is achieved by padding the input feature map with zeros, and then applying a convolution operation with a learnable kernel. The kernel is learned during training to upsample the feature map in the desired way. ConvTranspose2d can also be used for image generation or synthesis. By using a random noise vector as input, the layer can learn to generate realistic images. This process is often called generative adversarial networks (GANs). In summary, ConvTranspose2d is a powerful tool in deep learning for upsampling, deconvolution, and image generation tasks.

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