Comments (2)
作者您好。我对你们团队的工作很感兴趣。在spikformer v2中。为什么采用ANN作为Decoder?您在论文中只解释道用ANN的强大建模能力来提高SNN的表征能力。但Decoder自身的能力越强,对其输入的要求不就越低吗,也就是对Encoder输出的表征要求越低,这如何有利于Encoder的学习?恳请指教。
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Hello author. I'm interested in your team's work. In spikformer v2. Why use ANN as a Decoder? In your paper, you only explain that the powerful modeling capabilities of ANNs can be used to improve the characterization of SNNs. However, the stronger the ability of the Decoder itself, the lower the requirements for its input, that is, the lower the representation requirements for the output of the Encoder, how is this conducive to the learning of the Encoder? Please advise.
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Related Issues (20)
- 运行环境问题
- Error of "enumerate(loader) " in line 647 of "imagenet/train.py" HOT 2
- dvs128 code
- assert cupy is not None, 'CuPy is not installed! HOT 9
- 数据处理 HOT 3
- 学习率初始化值问题 HOT 2
- how to get the pretrained weights of the models
- Fail to Reproduce SpikFormer-8-512 on Imagenet HOT 4
- Fail to reproduce the Spikformer-4-256 on CIFAR-10 HOT 2
- Confused about the use of layernormalization in your Imagenet training code.
- Power calculation HOT 2
- 对spikformerv2的疑问
- 关于spikformer应用于时间序列的想法
- v2代码什么时候能开源呢
- Pretrained Weight Access
- Connecting Function on CIFAR10-DVS for SpikformerV2
- Energy consumption calculation on CIFAR10 dataset
- Problem about environment HOT 1
- spikformer-main\cifar10\model中模型没有位置嵌入操作
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