Comments (6)
But I think this speed is unacceptable in actual use. Moreover, only small data sets are used in the experiment. If semantic segmentation, imagenet, and high-resolution images tasks are used, the computational complexity is very large. It is estimated that GAN cannot reasonably infer the distribution equivalent to real data.
Yeah, you've made a point. ImageNet would be substantially harder. It definitely has a long road before practical use. But Rome is not built in one day. I think this paper can be a good start.
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(I am not among the authors. Just for discussion.) I think this could be normal. One possible reason is that the data is not real. The information per sample can be limited, so basically the student network needs to see many more samples than the training on real data.
from efficient-computing.
But I think this speed is unacceptable in actual use. Moreover, only small data sets are used in the experiment. If semantic segmentation, imagenet, and high-resolution images tasks are used, the computational complexity is very large. It is estimated that GAN cannot reasonably infer the distribution equivalent to real data.
from efficient-computing.
(I am not among the authors. Just for discussion.) I think this could be normal. One possible reason is that the data is not real. The information per sample can be limited, so basically the student network needs to see many more samples than the training on real data.
欢迎star我的仓库一起交流KD:https://github.com/FLHonker/Awesome-Knowledge-Distillation
from efficient-computing.
But I think this speed is unacceptable in actual use. Moreover, only small data sets are used in the experiment. If semantic segmentation, imagenet, and high-resolution images tasks are used, the computational complexity is very large. It is estimated that GAN cannot reasonably infer the distribution equivalent to real data.
Yeah, you've made a point. ImageNet would be substantially harder. It definitely has a long road before practical use. But Rome is not built in one day. I think this paper can be a good start.
我也一直试图改进这个问题,除非抛弃GAN,GAN的训练是个痛点。data-free是个很有意思的topic。
from efficient-computing.
Thanks for MingSun-Tse's answer. That's right. We will develop a more efficient data-free learning method in the future work.
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Related Issues (20)
- missing labels and low evaluation HOT 3
- "_call_impl(self, *input, **kwargs)" forward abnormally。
- Assertion `index >= -sizes[i] && index < sizes[i] && "index out of bounds"` failed. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. HOT 1
- 【Function get_box_metrics(self) 】Unabel to get repr for <class 'torch.Tensor'>
- question about number of layers HOT 1
- Feature map size Question? HOT 2
- The script of generating text for GPT4Image HOT 1
- hyper-parameter "--fuse_ab" in gold-yolo HOT 1
- Creating textual descriptions using BLIP-2 on Cifar-10/100 HOT 1
- Code for DAFL HOT 1
- when I train myself datasets ,a bad question was happened that my iou_loss dfl_loss and ap is zero HOT 2
- 蒸馏的时候问题:AttributeError: 'Trainer' object has no attribute 'loss_items' HOT 1
- 训练报错 HOT 1
- About parameter use_syncbn and fuse_ab in train.py HOT 1
- Gold-YOLO的运行 HOT 1
- gold_yolo怎么训练自己的数据集 HOT 1
- gold_yolo训练自己的数据集失败 HOT 1
- class Inferer 参数webcam, webcam_addr,调用时没赋值? HOT 1
- Gold-yolo推理时的精度非常差
- Does the model support OBB detection? HOT 2
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