The masked data is then passed through MAST's three-path encoder-decoder structure, leveraging a multi-path Swin-Unet architecture that simultaneously captures time-domain, frequency-domain, and magnitude-based features of the underlying HD-sEMG signal. These augmented inputs are then used in a ...
Speech enhancementU-NetSwin-transformerDeep learningenhancement performance has improved significantly with the introduction of deep learning models, especially methods based on the Long–Short-Term Memory architecture. However, these ...doi:10.1007/s00034-024-02736-9ZhangZipeng...
首先,使用纯卷积模型ConvNeXt在ImageNet上验证SparK,分别比较较小的模型(ViT、Swin、ConvNeXt)-S和较大的模型(ViT、Swin、ConvNeXt)-B. 通过对表1中的垂直结果进行比较,可以发现经过SparK预训练的卷积模型的性能明显由于基于Transformer的预训练方法(+0.7~2.7%)。尽管SparK既没有使用外部模型(DALL-E dVAE)也没有...
分割的UNet,DeepLab,SegNet等。SSL的MOCO、SimCLR等。Attention的Swin Transformer等。
如图2所示,作者比较了Swin Transformer中的Patch Merging与我们的Patch Ghosting。Patch Merging通过下采样技术合并相邻补丁来降低特征图的分辨率。然而,病理图像的分辨率常受染色和切片的影响,这可能限制了它们的表示。因此,我们的Patch Ghosting生成鼓励模型捕获相似细胞的多样化特征表示的特征图。此外,使用残差连接加速了...
We also providepretrain/viz_spconv.ipynbthat shows the "mask pattern vanishing" issue of dense conv layers. What's new here? 🔥 Pretrained CNN beats pretrained Swin-Transformer: 🔥 All models can benefit, showing a scaling behavior: ...
the Swin transformer in the base and large model regimes but outperforms Swin in the huge model regime. 7. Conclusion In this paper, we introduce a new ConvNet model family called ConvNeXt V2 that covers a broader range of com- plexity. While the archite...
encoder用来当作tokenizer,把图像离散化(对应一个个patch),然后给Transformer输入patch,预测离散后的...
This work proposes a prostate gland segmentation framework that utilizes a dual-path Swin Transformer UNet structure and leverages Masked Image Modeling for large-scale self-supervised pretaining. A tumor-guided self-distillation step further fused the binary tumor labels of each patch to the encoder...
DRUNetDilated Residual UNet SNRSignal-to-Noise Ratio SwinIRImage Restoration Using Swin Transformer MDTAMulti-Dconv Head Transposed Attention GDFNGated-Dconv Feed-forward Network EWTEfficient Wavelet Transformer NEFNeighborhood Feature Enhancement