250 points by darkhorse_research 1 year ago flag hide 11 comments
yongqi 1 year ago next
Fascinating topic! I'm curious how this can be applied to reduce model size and improve inference speeds for edge devices.
deeplearningnerd 1 year ago next
Yes, pruning has huge potential when it comes to feasibility of deploying large models on hardware with limited resources. Have you tried working with TensorFlow Lite?
yongqi 1 year ago prev next
Not yet, I've been exploring PyTorch options for pruning. I'll definitely check TensorFlow Lite out. Thanks for the recommendation!
datagram 1 year ago prev next
Neural Network pruning can also help prevent overfitting, correct? By eliminating the less effective weights, we can improve the generalization ability of the model.
smathew 1 year ago next
Absolutely! Model regularization is another key benefit of pruning. Combined with techniques like Dropout, Network Pruning can help the model to focus more on robust features, rather than artifacts in the training data.
net_fisherman 1 year ago prev next
Has any research been done on fine tuning the models after pruning? I'm wondering if performance could be affected if we trained without first pruning.
mlfollower 1 year ago next
There are some interesting papers that show fine tuning the pruned models can result in similar or even better performance compared to non-pruned models. The key aspect is to ensure the right learning rate when fine-tuning.
tensor_pruner 1 year ago prev next
Can we get some insights on measuring the performance of pruned models? What are some standard metrics and approaches worth considering?
data_analyst 1 year ago next
When evaluating pruned models, typically, you would want to consider factors like FLOPs reduction, parameter count reduction, and accuracy changes. These metrics provide us a better understanding of the trade-offs when selecting pruning strategies.
topology_optimizer 1 year ago prev next
I wonder what the most promising directions are in this field for the next few years. We've progressed quite a bit in pruning already, but are there techniques or methods waiting to be explored further?
fast_computation 1 year ago next
Several promising avenues to explore: - Layer-wise pruning techniques that selectively layer prune (rather tha