DNN(deep neural networks)在计算机视觉任务中取得了很好的效果,比如图像分类、目标检测、实例分割等。不过,大量的参数和计算的复杂度带来的高存储和高计算性能的限制,使得DNN很难应用在一些低性能的设备上。为了解决这个问题,提出了很多压缩技术:network pruning,l
https://github.com/wonnado/binary-netsPytorch Binary Ensemble Neural Network 的思路就是将 若干个独立的二值网络组合起来近似 实数值网络,本文提出的 Structured Binary Neural Networks 更进一步,将实数值网络分解为各个模块,然后使用若干二值网络来近似这些模块,对于网络的分解,通过学习完成动态分解。 3.1 Problem ...
虽然大多数现有的BNN [3, 6, 32, 33, 35, 41, 49]仍然不如32位ResNet-18(69.74%的顶级精度)[14],但BNext-XL将BNN的上限提高到80.57%的关键精度水平。因此,它以10%的精度超过了大多数现有的工作,并实现了接近SOTA 32位设计的结果,如ConvNext [29]、Swin Transformer [27]和RegNetY-4G [40]。与之前...
GGAC: Multi-relational image gated GCN with attention convolutional binary neural tree for identifying disease with chest X-raysMulti-relational graphGated graph convolutional networkIdentifying diseaseAttention transformerUsing medical images for disease identification is an important application in the medical...
One reason MEB works so well as a complement to Transformer-based deep learning models for search relevance is that it can map single facts to features, allowing MEB to gain a more nuanced understanding of individual facts. For example, many deep neural ...
Attention transformer. 注意力转换器模块用于强制网络捕捉判别特征,见图3。根据深度网络[30]的经验接受域远小于理论接受域的事实,具有区分度的表征应该是由我们所提出的树状结构的新层次上的更大的接受域形成的。为此,我们将 Atrous Spatial Pyramid Pooling (ASPP) 模块[5]集成到注意力转换器中。具体来说,ASPP模...
We also tried dis- tilling from a vision transformer [12], but surprisingly the result is similar. In order to measure the accuracy, after decaying the learning rate to zero, we continue training for a few epochs. We observe both top-1 oscillating due to the batch normal- ization ...
Skeleton-based action recognition with directed graph neural networks K. Cheng et al. Skeleton-based action recognition with shift graph convolutional networkView more references Cited by (12) Decoupled spatio-temporal grouping transformer for skeleton-based action recognition 2024, Visual Computer Non-loc...
(F1:0.953) are obtained when the DPCNN model is used on both pseudo-code and string, so we use this model in all subsequent experiments. Transformer-based accuracy is low (F1:0.846) because our limited resources make it difficult for us to train Transformer models with large parameters. In...
Wang H, Qu W, Katz G, Zhu W, Gao Z, Qiu H, Zhuge J, Zhang C (2022) Jtrans: jump-aware transformer for binary code similarity detection. In: Proceedings of the 31st ACM SIGSOFT international symposium on software testing and analysis. ISSTA 2022, pp 1–13. Association for Computing ...