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Dimensionality Reduced Training by Pruning and Freezing Parts of a Deep Neural Network, a Survey
By freezing weights, the number of trainable parameters is shrunken which reduces gradient computations and the dimensionality of the model's optimization space.
Interspace Pruning: Using Adaptive Filter Representations to Improve Training of Sparse CNNs
Finally, we show that advances of IP are due to improved trainability and superior generalization ability.
COPS: Controlled Pruning Before Training Starts
The combinatorial optimization problem given by COPS is relaxed on a linear program (LP).
FreezeNet: Full Performance by Reduced Storage Costs
On the classification tasks MNIST and CIFAR-10/100 we outperform SNIP, in this setting the best reported one-shot pruning method, applied before training.
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