Comments (4)
he-y
commented on June 6, 2024
You are right.
This operation is for the compatibility of soft filter pruning.
Please take a look at this reply.
from filter-pruning-geometric-median.
sseung0703
commented on June 6, 2024
Ok, these things are for soft filter pruning. But why they are explained in your new paper?
Even, you put algorithm about it in paper.
from filter-pruning-geometric-median.
he-y
commented on June 6, 2024
Yes.
FPGM is about pruning criteria, and soft filter pruning is about pruning mechanism.
The algorithm in this paper is for a general framework containing both mechanisms.
from filter-pruning-geometric-median.
he-y
commented on June 6, 2024
I am not quite sure about your question.
You want the pruned index filter change or not?
from filter-pruning-geometric-median.
Related Issues (20)
- Pruning result on ResNet-18 of Imagenet HOT 1
- Model size problem
- Question about baseline on ImageNet HOT 5
- Problems on the implementation HOT 1
- about training time HOT 4
- implementation on efficientnet HOT 2
- Experiment Question
- About function get_filter_similar() HOT 7
- Confusion with init_rate HOT 1
- Accuracy of pruned vgg without pruning
- Result of resnet18 on imagenet is low until epoch60
- 你好，有个代码段没理解 HOT 8
- TypeError: 'module' object is not callable HOT 2
- small model is not small HOT 6
- Can model compression be used for GAN？
- How to integrate FPGM and SFP
- 公布的模型参数怎么理解 HOT 1
- 作者提供的训练的模型，云盘下载之后，解压内部tar文件的时候显示资源有问题，解压失败 HOT 1
- 您好！请问get_small_model.py文件如何使用？谢谢！ HOT 1
- FPGM on object detection model & implementation of NNI HOT 1
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from filter-pruning-geometric-median.