该文主要是分析和讨论了跳跃连接的一些局限,同时分析了BN的一些限制,提出了通过递归的Skip connection和layer normalization来自适应地调整输入scale的策略,可以很好的提升跳Skip connection的性能,该方法在CV和NLP领域均适用。 1简介 Skip connection是一种广泛应用于提高深度神经网络性能和收敛性的技术,它通过神经网络层...
层归一化在一定程度上有助于解决 expanded skip connection带来的优化问题。 本文提出的带有LN的recursive skip connection,通过将expanded skip connection划分为多个阶段,以更好地融合转换输入的效果,进一步简化了优化过程。 利用Transformer在WMT-2014 EN-DE机器翻译数据集上的实验结果进一步证明了递归架构的有效性和效率,...
更利于生成分割mask针对resnet里的skip connection:1.从Resnet最早引入skip-connection的角度看，...
1.从Resnet最早引入skip-connection的角度看，这种跳跃连接可以有效的减少梯度消失和网络退化问题，使训练...
Grouping skip connectionDeep NMTTransformerMost of the deep neural machine translation (NMT) models are based on a bottom-up feedforward fashion, in which representations in low layers construct or modulate high layers representations. We conjecture that this unidirectional encoding fashion could be a ...
缺乏skip connection,資訊流較弱。 建議實驗設計 如果你想更科學地評估: 你也可以試著加入小型TransformerBlock在 PixelShuffle 後,效果可能更好(但成本高)。 延伸組合建議(建議一套輕量上採樣模組) class PixelShuffleUpBlock(nn.Module): def __init__(self, in_channels, out_channels, upscale_factor=2, us...
The skip connection keeps early layer features, improving the model’s learning capabilities, by adding the output of the first and third convolutional blocks. This operation is performed in the adding layer, then the output of this is forwarded to the dropout layer and further, the flatten ...
FSCA-Net is a novel U-shaped network architecture that utilizes the Parallel Attention Transformer (PAT) to enhance the extraction of spatial and channel features in the skip-connection mechanism, further compensating for downsampling losses. We design the Cross-Attention Bridge Layer (CAB) to ...
Each re-iteration aims to improve performance by employing a denser skip connection mechanism that harnesses multi-scale features for accurate object mapping. However, denser connections increase network parameters and do not necessarily contribute to precise segmentation. In this paper, we develop three...
ModelBaseline3×10−93×10−83×10−73×10−6Improv. ResNet5079.279.079.479.279.3+0.2 ViT-S/16-22477.878.078.178.077.8+0.3 DeIT-S/16-22479.979.980.180.079.8+0.2 Swin-S/4-7-22483.383.283.583.383.1+0.2 Table 3: Theaccuracyof ResNet over different depths on CIFAR10, CIFAR100 and Image...