Our latest SignalPop AI Designer release, version 0.9.1.70, now supports Domain-Adversarial Neural Networks (DANN) as described by [1].
By adding image overlay support to an updated MNIST Dataset Creator, you can now create both source and target datasets.
Using the new source and target dataset support you can now easily create DANN networks that use both.
An updated visual editor also supports multiple source and target datasets as shown below with the full DANN model.
The newly added GRADIENTSCALER layer allows for easy gradient reversal which is then added to the bottleneck layer, shown above, to create an adversarial relationship between the two networks.
Try creating a DANN yourself with the easy step-by-step Tutorials that show you how to get up and running with the latest version of the SignalPop AI Designer.
New General Features
- Projects now optionally support both source and target datasets.
- A new GRADIENTSCALLER layer has been added for gradient reversals.
- Full DANN solver and model templates have been added.
- The MNIST Dataset Creator can now create datasets with an image overlay.
- We now support the recently released NVIDIA cuDNN 7.1.3.
New Debugging Features
- We have added single stepping support for both training and testing.
- A new blob data debugger shows the contents of each blob passing between layers.
- The model editor has been improved to show models viewable by phase (TRAIN, TEST and RUN).
Happy ‘deep’ learning!
References
[1] Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., . . . Lempitsky, V. (2016). Domain-Adversarial Training of Neural Networks. Journal of Machine Learning Research 17, 1-35.