4,534 research outputs found
Data-free parameter pruning for Deep Neural Networks
Deep Neural nets (NNs) with millions of parameters are at the heart of many
state-of-the-art computer vision systems today. However, recent works have
shown that much smaller models can achieve similar levels of performance. In
this work, we address the problem of pruning parameters in a trained NN model.
Instead of removing individual weights one at a time as done in previous works,
we remove one neuron at a time. We show how similar neurons are redundant, and
propose a systematic way to remove them. Our experiments in pruning the densely
connected layers show that we can remove upto 85\% of the total parameters in
an MNIST-trained network, and about 35\% for AlexNet without significantly
affecting performance. Our method can be applied on top of most networks with a
fully connected layer to give a smaller network.Comment: BMVC 201
Automated Pruning for Deep Neural Network Compression
In this work we present a method to improve the pruning step of the current
state-of-the-art methodology to compress neural networks. The novelty of the
proposed pruning technique is in its differentiability, which allows pruning to
be performed during the backpropagation phase of the network training. This
enables an end-to-end learning and strongly reduces the training time. The
technique is based on a family of differentiable pruning functions and a new
regularizer specifically designed to enforce pruning. The experimental results
show that the joint optimization of both the thresholds and the network weights
permits to reach a higher compression rate, reducing the number of weights of
the pruned network by a further 14% to 33% compared to the current
state-of-the-art. Furthermore, we believe that this is the first study where
the generalization capabilities in transfer learning tasks of the features
extracted by a pruned network are analyzed. To achieve this goal, we show that
the representations learned using the proposed pruning methodology maintain the
same effectiveness and generality of those learned by the corresponding
non-compressed network on a set of different recognition tasks.Comment: 8 pages, 5 figures. Published as a conference paper at ICPR 201
Learning to Prune Deep Neural Networks via Layer-wise Optimal Brain Surgeon
How to develop slim and accurate deep neural networks has become crucial for
real- world applications, especially for those employed in embedded systems.
Though previous work along this research line has shown some promising results,
most existing methods either fail to significantly compress a well-trained deep
network or require a heavy retraining process for the pruned deep network to
re-boost its prediction performance. In this paper, we propose a new layer-wise
pruning method for deep neural networks. In our proposed method, parameters of
each individual layer are pruned independently based on second order
derivatives of a layer-wise error function with respect to the corresponding
parameters. We prove that the final prediction performance drop after pruning
is bounded by a linear combination of the reconstructed errors caused at each
layer. Therefore, there is a guarantee that one only needs to perform a light
retraining process on the pruned network to resume its original prediction
performance. We conduct extensive experiments on benchmark datasets to
demonstrate the effectiveness of our pruning method compared with several
state-of-the-art baseline methods
Fast ConvNets Using Group-wise Brain Damage
We revisit the idea of brain damage, i.e. the pruning of the coefficients of
a neural network, and suggest how brain damage can be modified and used to
speedup convolutional layers. The approach uses the fact that many efficient
implementations reduce generalized convolutions to matrix multiplications. The
suggested brain damage process prunes the convolutional kernel tensor in a
group-wise fashion by adding group-sparsity regularization to the standard
training process. After such group-wise pruning, convolutions can be reduced to
multiplications of thinned dense matrices, which leads to speedup. In the
comparison on AlexNet, the method achieves very competitive performance
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