532 points by ai_guru 1 year ago flag hide 25 comments
john123 1 year ago next
Fascinating approach! I've been following the developments in neural network pruning and this really caught my attention.
ai_specialist 1 year ago next
Could you elaborate more on the potential benefits of this method for real-world applications?
john123 1 year ago next
Certainly! It could help reduce computational costs and improve inference times significantly. With less data required for training the network, there could also be advantages in terms of data storage requirements.
machine_learning_enthusiast 1 year ago prev next
I agree, this method has great potential. What kind of architecture does this technique work best with, and can it be extended to different architectures?
john123 1 year ago next
Great question! The authors mentioned it was tested on both convolutional and recurrent neural networks, and the concept can potentially be applied to any architecture. This method should be universally applicable, but certain architectures might require adjustments.
ai_specialist 1 year ago next
I heard this is also related to the topic on model compression. Is there any overlap between the techniques used in pruning and model compression?
john123 1 year ago next
Indeed, there is a connection between neural network pruning and model compression. Pruning can be seen as one of the steps involved in attaining model compression, by making the network smaller, allowing quantization and other compression techniques to be applied more effectively.
machine_learning_enthusiast 1 year ago prev next
This pruning technique is amazing, I've been looking into implementations in our products to increase efficiency and reduce complexity.
nn_exploreri 1 year ago next
It looks like this method is based on weight magnitude pruning, which has been a popular technique lately. Can we expect further performance improvements from structured pruning?
john123 1 year ago next
That's an excellent question, nn_explorer. Some researchers argue that certain types of structured pruning, like channel pruning, can achieve better performance improvements while preserving the network structure.
professor_x 1 year ago prev next
I find it to be especially relevant for deploying large models to resource-constrained devices, like mobile phones or IoT gadgets.
ai_specialist 1 year ago next
I wonder how transfer learning can be util
ai_specialist 1 year ago next
These findings are significant as they support recent resea
machine_learning_enthusiast 1 year ago next
I've been looking through papers and it seems like the authors have shared their code on GitHub – anyone already test it out?
ai_specialist 1 year ago next
I've been playing around with the authors' code a bit, and I was able to reproduce the results they published in their paper. If you're interested in the comparisons, I would highly recommend checking out the Appendix where they discuss related works on this topic.
deepthoughts 1 year ago next
I'm trying to figure out if there are any particular reasons why the authors didn't include specific pruning percentages in their image classification example.
deepthoughts 1 year ago next
I've tested the code for this pruning method on a simple ConvNet and got promising results. But I'm having difficulties applying it to a more complex ResNet model.
nn_exploreri 1 year ago next
For your ResNet problem, I think you have to modify the sparsity settings based on the architecture and layer types. Might want to look up how to do so in the official TensorFlow documentation on pruning.
deepthoughts 1 year ago next
Thanks, nn_explorerI. What I think I'm missing is how to set those sparsity settings appropriately, but I'll read more about it to get a clearer idea. Any resources you might recommend?
nn_exploreri 1 year ago next
Deepthoughts, I'd suggest this tutorial by TensorFlow: https://www.tensorflow.org/lite/performance/pruning. It covers exactly what you're looking for.
stan_gradient 1 year ago prev next
Pruning has been discussed for quite some time in the research community. But it's interesting to see more practical implementations and quantifiable results coming up.
professor_x 1 year ago next
I've been discussing this with my research group and we think it may also have applications to edge computing use cases.
john123 1 year ago next
Absolutely true, Professor X! It opens doors for more capable models in software and hardware that we couldn't have considered before.
deepthoughts 1 year ago prev next
How does this technique compare with other pruning techniques like dynamic network surgery?
nn_exploreri 1 year ago next
Deepthoughts, I think both weight magnitude and dynamic network surgery have their merits, but they may serve different use cases best, similar to how different optimization algorithms help in different situations.