Comments (4)
I can get the message. please help me
from caffe-deepbinarycode.
Hi @sunyinhui
Sorry. Since this project is a work in progress, some parameter names may confuse. We will improve this as soon as possible.
The red and green parts represent two different objective functions. They are not layers, so they don't have "nodes".
The green one represents the K1_EuclideanLoss
, and the red one is K2_EuclideanLoss
.
K1_EuclideanLoss
will enforce each node in encode_neuron
to be 0 or 1.
K2_EuclideanLoss
will ensure each node in encode_neuron
has a 50% chance of being 0 or 1.
Btw, our binary codes are learn in the encode_neuron
. During testing, we extract the binary codes from encode_neuron
Should you have any question about the paper, please feel free to email me.
from caffe-deepbinarycode.
Oh , Thanks! ^_^
from caffe-deepbinarycode.
Hi Kevinlin,I can not underdtand deeply the loss_beta and loss_gamma . Please help me. Thanks
from caffe-deepbinarycode.
Related Issues (20)
- softmax in cifar10 HOT 1
- Can I run Caffe-DeepBinaryCode on non-GPU machine? HOT 1
- How can I train on some new CNNs such as Resnet or inception? HOT 5
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- the weight of loss function HOT 3
- How did you pre-process the MNIST dataset as it is one channel? HOT 1
- How can I get the path ./matlab/caffe/matcaffe_batch_feat.m? HOT 6
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- Cannot converge when I training with cifar10 HOT 6
- About NUS-WIDE dataset HOT 1
- i trained on my own dataset,bu the accuracy is 0.058
- Issue about the classification error
- Sorrry,do you hava python3 and tensorflow version? i don't know caffe, if you hava that version,please give me,thank you HOT 3
- Hello,how do you dispose multi_label dataset?,like UT-ZAP50K and NUS-WIDE? Can you tell me in detail?
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from caffe-deepbinarycode.