alias-free-gan
norse
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| alias-free-gan | norse | |
|---|---|---|
| 1 | 6 | |
| 77 | 607 | |
| - | 3.3% | |
| 0.0 | 6.5 | |
| 9 months ago | 17 days ago | |
| Python | Python | |
| MIT License | GNU Lesser General Public License v3.0 only |
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alias-free-gan
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When is the alias-free GAN code going to be released?
And do you necessarily need the original NVidia implementation? It will be under a limited NVidia Source Code Licence anyway. Alias-free GAN has been already implemented by Rosinality https://github.com/rosinality/alias-free-gan-pytorch (MIT license) and is maintained and extended by duskvirkus https://github.com/duskvirkus/alias-free-gan
norse
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Neuromorphic learning, working memory, and metaplasticity in nanowire networks
This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...
Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.
Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.
I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?
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[D] The Complete Guide to Spiking Neural Networks
Surrogate gradients and BPTT, this is what is implemented in Norse https://github.com/Norse/Norse. It is also possible to compute exact gradients using the Eventprop algorithm.
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Don't Mess with Backprop: Doubts about Biologically Plausible Deep Learning
That repo is slightly outdated, development now continues at https://github.com/norse/norse.
What are some alternatives?
snntorch - Deep and online learning with spiking neural networks in Python
Spiking-Neural-Network - Pure python implementation of SNN
spikingjelly - SpikingJelly is an open-source deep learning framework for Spiking Neural Network (SNN) based on PyTorch.
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
bindsnet - Simulation of spiking neural networks (SNNs) using PyTorch.
Neuromorphic-Computing-Guide - Learn about the Neumorphic engineering process of creating large-scale integration (VLSI) systems containing electronic analog circuits to mimic neuro-biological architectures.
ocaml-torch - OCaml bindings for PyTorch
lava - A Software Framework for Neuromorphic Computing
MegEngine - MegEngine 是一个快速、可拓展、易于使用且支持自动求导的深度学习框架
stable-karlo - Upscaling Karlo text-to-image generation using Stable Diffusion v2.
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
lamp - deep learning and scientific computing framework with native CPU and GPU backend for the Scala programming language