Paper can be found here: https://arxiv.org/pdf/1903.12272.pdf

I am currently investigating the research into Spiking Neural Networks using Spike-timing-dependent plasticity learning, initially regarding image processing.

I have now read several papers that discuss “Spiking Convolutional Neural Networks”, and cannot understand how these particular networks can be convolutional in nature, at least in the same way as backprop trained CNNs are.

The kernels in the standard CNN are trained by backprop against every possible patch or feature in the preceding layer. So a kernel can detect a feature at any point in the preceding layer.

While you can definitely use “kernels” with these spiking networks, they would only apply to a set patch of the image right? If you wanted something that ran over all the patches your “kernel neuron” would just end up with a link from each neuron in the previous layer and it would be a mess. Or you would need to duplicate this kernel neuron x number of times depending on the size of the input, and find some way to keep these neurons with the same input weights.

What am I misunderstanding here? Do you simply end up with a whole pile of duplicate kernels across all the patches? This would definitely work but is it optimal?

Hi,

Do you know some audiobooks (or podcast, preferably specific episodes) related to AI that are not only targeted towards beginners ?
I really enjoyed “Algorithms to live by” which is computer science related and “What if” even though it’s hard to follow with this format.

Thank you !

Hi! Just would like to share with you a project that I have been working for some time:

• embedded-ml

A bit of background (ONNX)

In short, onnx provides an Open Neural Network Exchange format. This format, describes a huge set of operators, that can be mixed to create every type of machine learning model that you ever heard of, from a simple neural network to complex deep convolutional networks. Some examples of operators are: matrix multiplications, convolutions, adding, maxpool, sin, cosine, you name it! They provide a standardised set of operators here. So we can say that onnx provides a layer of abstraction to ML models, which makes all framework compatible between them. Exporters are provided for a huge variety of frameworks (PyTorch, TensorFlow, Keras, Scikit-Learn) so if you want to convert a model from Keras to TensorFlow, you just have to use Keras exporter to export Keras->ONNX and then use the importer to import ONNX-TensorFlow.

The project

There are many open-source repos that can run inference on ML models with C code, but most of them are framework-specific, so you are tied to TensorFlow or whatever framework. The idea behind this project is to have a “backend” that can run inference on ONNX models. You might have heard of “onnxruntime” which provides runtimes to run inference on ONNX models in different languages, like in R, Go or even C++, but the idea of this project is to have a pure C99 runtime without any external dependency, that can compile with old compilers for any device without any fancy hw accelerators, multicore or GPUs.

What’s next?

The project is in a very early stage and we are looking for contributors, both for C code and general ideas. So far, you can run inference on the well known MNIST model for handwritten digits recognition. Inside the repo you can find some specific tasks and documentation about what we have so far.

We propose a simple yet efficient anchor-free instance segmentation, called CenterMask, that adds a novel spatial attention-guided mask (SAG-Mask) branch to anchor-free one stage object detector (FCOS) in the same vein with Mask R-CNN. Plugged into the FCOS object detector, the SAG-Mask branch predicts a segmentation mask on each box with the spatial attention map that helps to focus on informative pixels and suppress noise. We also present an improved VoVNetV2 with two effective strategies: adds (1) residual connection for alleviating the saturation problem of larger VoVNet and (2) effective Squeeze-Excitation (eSE) deals with the information loss problem of original SE. With SAG-Mask and VoVNetV2, we deign CenterMask and CenterMask-Lite that are targeted to large and small models, respectively. CenterMask outperforms all previous state-of-the-art models at a much faster speed. CenterMask-Lite also achieves 33.4% mask AP / 38.0% box AP, outperforming YOLACT by 2.6 / 7.0 AP gain, respectively, at over 35fps on Titan Xp. We hope that CenterMask and VoVNetV2 can serve as a solid baseline of real-time instance segmentation and backbone network for various vision tasks, respectively.

• arxiv
• code

I’m training a DCGAN on a dataset of 10k images. I want to expirement with adding and taking away convolutions in attempt to balance out the generator and discriminator. Whats the rule to do this in practice?

Here is my generator:

self.main = nn.Sequential( nn.ConvTranspose2d(100, 512, 4, 1, 0, bias = False), nn.BatchNorm2d(512), # We normalize all the features along the dimension of the batch. nn.ReLU(True), # We apply a ReLU rectification to break the linearity. nn.ConvTranspose2d(512, 256, 4, 2, 1, bias = False), nn.BatchNorm2d(256), # We normalize again. nn.ReLU(True), # We apply another ReLU. nn.ConvTranspose2d(256, 128, 4, 2, 1, bias = False), nn.BatchNorm2d(128), # We normalize again. nn.ReLU(True), # We apply another ReLU. nn.ConvTranspose2d(128, 64, 4, 2, 1, bias = False),
nn.BatchNorm2d(64), # We normalize again. nn.ReLU(True), # We apply another ReLU. nn.ConvTranspose2d(64, 3, 4, 2, 1, bias = False), nn.Tanh() )

What input and output values should I have for the 6th layer?