Comments (3)
I think we do not report any results on Cifar10 but use a more difficult dataset, ImageNet.
In the NUS-WIDE dataset, many image urls are invalid. Thus, the NUS-WIDE downloaded by us may be a little different than theirs. And they use the images associated with the 21 most frequent concept tags (classes), but we use all the images in the NUS-WIDE, which are associated with 81 concepts. Thus, our task is a little difficult than theirs, which causes the performance to be worse.
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Thanks for your interpretation!but In your professional brothers Yue Cao‘s paper:"HashGAN: Deep Learning to Hash with Pair Conditional Wasserstein GAN" they report the results on cifar10 using hashNet。the results(0.643(16bits)) is much lower than others(above 0.8)。the only differernt is that their backbone is CNN-F but not AlexNet。I recover your work with tensorflow, it also run up to 0.8。I feel so puzzled。
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You can ask them about the details of their experiments. Yue Cao's email is [email protected]
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Related Issues (20)
- Problem with MAP@k
- 你好,非常感谢分享的NUS数据集,但是下载解压发现数据的图片比原始要少 HOT 2
- Why take two batches as input HOT 4
- the mAP on cifar10 is only 0.30 HOT 5
- the scale tanh is useless HOT 1
- a question about training set HOT 1
- The mAP of ImageNet is different from that on paper HOT 7
- How did you generate the "train.txt" of MS-COCO? HOT 1
- The pretrained model HOT 1
- nuswide dataset HOT 1
- @bfan @caozhangjie HOT 1
- question on nuswide dataset
- Pytorch with Resnet50 HOT 6
- HashNet on CUB200 HOT 1
- Anybody willing to share a pretrained model (from ImageNet or CoCo or similar)?
- AlexNet backbone result (COCO) HOT 3
- False sampling of data HOT 1
- test.py for CIFAR HOT 1
- number of training images for imagenet HOT 1
- question on nuswide81 dataset HOT 1
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