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Results tagged with neural-networks
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user 201218
Artificial neural networks (ANNs) are a broad class of computational models loosely based on biological neural networks. They encompass feedforward NNs (including "deep" NNs), convolutional NNs, recurrent NNs, etc.
4
votes
0
answers
469
views
How do upsampling layers work for coarse-to-fine output in semantic segmentation?
Here is a figure illustrating the Fully Convolutionnal Network (FCN) of the Fully Convolutionnal Paper for Semantic Segmentation :
The upsampling layer at the end confuses me. I cannot understand how …
- 1,396
2
votes
0
answers
59
views
How are mean results on benchmark obtained when training neural networks?
In most neural network papers, networks are trained on a known database where state-of-the-art performance is known ("benchmark"). Whatever metric is chosen to illustrate the network's performance, of …
- 1,396
6
votes
1
answer
3k
views
When to stop training of neural network when validation loss is still decreasing but gap wit...
During training of CNNs, I often come across this case for training and validation loss :
X axis is epochs, Y axis is cross entropy loss.
I would like to keep the "best model", meaning the one which …
- 1,396
2
votes
1
answer
2k
views
Cross-entropy yields strange results when neural network gets too sure about his outputs
I'm using a classical CNN for image binary classification. Output is composed of two neurons, each giving the network's "raw output" for the 2 classes. So for an image, it would be eg $(0.62, -0.52)$. …
- 1,396
92
votes
Accepted
How is it possible that validation loss is increasing while validation accuracy is increasin...
Other answers explain well how accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw output (float) and a class (0 or 1 in the case of binary …
- 1,396