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Annotated Deep Learning Paper Implementations

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            <h1><a href="https://nn.labml.ai/conv_mixer/index.html">你只需要补丁吗？</a></h1>
<p>这是 <a href="https://pytorch.org">PyTorch</a> 对论文《<a href="https://papers.labml.ai/paper/2201.09792">补丁就是你所需要的？</a>》的实现</p>。
<p>convMixer 类似于 <a href="https://nn.labml.ai/transformers/mlp_mixer/index.html">MLP 混音器</a>。MLP-Mixer 通过在空间维度上应用 MLP，然后在信道维度上应用 MLP 来分离空间维度和信道维度的混音（空间 MLP 取代 <a href="https://nn.labml.ai/transformers/vit/index.html">vIT</a> 注意力，信道 MLP 是 ViT 的 <a href="https://nn.labml.ai/transformers/feed_forward.html">FFN</a>）。</p>
<p>ConvMixer 使用 1x1 卷积进行通道混合，使用深度卷积进行空间混合。由于它是卷积而不是整个空间的完整的 MLP，因此与 vIT 或 MLP-Mixer 相比，它只混合附近的批次。此外，MLP-Mixer 每次混合使用两层 MLP，ConvMixer 每次混合使用单层。</p>
<p>该论文建议删除信道混合（逐点卷积）上的剩余连接，在空间混合（深度卷积）上仅使用残差连接。他们还使用<a href="https://nn.labml.ai/normalization/batch_norm/index.html">批量标准化</a>而不是<a href="../normalization/layer_norm/index.html">图层标准化</a>。</p>
<p>这是<a href="https://nn.labml.ai/conv_mixer/experiment.html">一项在 CIFAR-10 上训练 ConvMixer 的实验</a>。</p>
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