Comments (4)
Thanks for your interests. Because we expect adding the layer activations will eliminate some information. Explicitly keeping all of previous layers' activations provides more useful information for later layers. That's the major difference between DenseNets and ResNets.
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Ah okay, this is what I suspected as well; do you think any computation saved by adding the layer activations would counteract the generality of concatenating the two?
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Our experiments suggested well-designed transformations (See our paper, DenseNet-BC structure) performed on concatenated features can save parameters and computation, compared with ResNet.
Maybe there's a balance between adding and concatenating that can maximize the computation savings.
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Ah okay, I see. That's great to hear! Thanks for the explanations (the paper is awesome!) 👍
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Related Issues (20)
- Covolution before entering the first dense block for imagenet dataset HOT 1
- DenseNet on Pascal VOC HOT 2
- results on cifar100 HOT 1
- I tried to reproduce Wide-DenseNet-BC results on cifar10, but got 0.5% more than your error HOT 4
- Why is composite function BN-ReLU-Conv3x3 ? HOT 1
- Pretrained weights for the 0.8M parameters config HOT 1
- Why not share the first BN and ReLU? HOT 2
- The layers within the second and third dense block don't assign the least weight to the outputs of the transition layer in my trained model
- Why we can detach any layer without affecting others in densenet?
- question about standardization HOT 6
- cifar validation loss decrease than increase after learning rate change HOT 4
- Question on channel before entering the first block HOT 2
- Question on impede information flow HOT 1
- Is there a pretrained CIFAR 100 or CIFAR 10 model? HOT 2
- Densenet on CIFAR training from scratch
- Question on the last transition layer
- Receptive field of DenseNet
- image classification
- cannot open </cifar-10-python/data_batch_1>
- Different DensceNet
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