Comments (1)
It's more specifically the dot product amongst the two vectors s.t. we enforce priors conditionally on all outputs. If you believe it's wrong though, definitely just comment again and I'll read over the portions of the paper again since it's been awhile.
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Related Issues (20)
- Syntax error: invalid syntax on line 65 HOT 1
- AttributeError: module 'torch.sparse' has no attribute 'torch' HOT 1
- Why dim=2 here? I guess the softmax is taken over all capsules in the layer above, so shouldn't that be dim=0? HOT 4
- Deviations of this implementation from the original paper HOT 4
- How should I set num_route_nodes? HOT 2
- Can it support pytorch version 0.4 or latest?
- How to replace dataset and change dimensions HOT 1
- RuntimeError: The size of tensor a (16) must match the size of tensor b (10) at non-singleton dimension 1 HOT 1
- Can the 8 32-channel conv be replaced with a single 256-channel conv in PrimaryCapsule? HOT 1
- MLP Capsules ? HOT 1
- the visdom window has nothing to show HOT 1
- Squash function HOT 1
- Try my implementation HOT 1
- @ operator HOT 2
- No such file or directory
- Detach() in route?
- How to prove it is a real capsule network?
- What are the core code of capsule network?
- RuntimeError: Input type (torch.cuda.ByteTensor) and weight type (torch.cuda.FloatTensor) should be the same
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