### DropPruning for Model Compression

Deep neural networks (DNNs) have dramatically achieved great success on a variety of challenging tasks. However, most of the successful DNNs are structurally so complex, leading to much storage requirement and floating-point operation. This paper proposes a novel technique, named Drop Pruning, to compress the DNNs by pruning the weights from a dense high-accuracy baseline model without accuracy loss. Drop Pruning also falls into the standard iterative prune-retrain procedure, where a \emph{drop} strategy exists at each pruning step: \emph{drop out}, stochastic deleting some unimportant weights and \emph{drop in}, stochastic recovering some pruned weights. \emph{Drop out} and \emph{drop in} are supposed to handle the two drawbacks of the traditional pruning methods: local importance judgment and irretrievable pruning process, respectively. The suitable choosing of \emph{drop} probabilities can decrease the model size during pruning process and lead it to flow to the target sparsity. Drop Pruning also has some similar spirits with dropout, a stochastic algorithm in Integer Optimization and the Dense-Sparse-Dense training technique. Drop Pruning can significantly reducing overfitting while compressing the model. Experimental results demonstrates that Drop Pruning can achieve the state-of-the-art performance on many benchmark pruning tasks, about ${11.1\times}$ compression of VGG-16 on CIFAR10 and ${14.3\times}$ compression of LeNet-5 on MNIST without accuracy loss, which may provide some new insights into the aspect of model compression.

### A "Network Pruning Network" Approach to Deep Model Compression

We present a filter pruning approach for deep model compression, using a multitask network. Our approach is based on learning a a pruner network to prune a pre-trained target network. The pruner is essentially a multitask deep neural network with binary outputs that help identify the filters from each layer of the original network that do not have any significant contribution to the model and can therefore be pruned. The pruner network has the same architecture as the original network except that it has a multitask/multi-output last layer containing binary-valued outputs (one per filter), which indicate which filters have to be pruned. The pruner's goal is to minimize the number of filters from the original network by assigning zero weights to the corresponding output feature-maps. In contrast to most of the existing methods, instead of relying on iterative pruning, our approach can prune the network (original network) in one go and, moreover, does not require specifying the degree of pruning for each layer (and can learn it instead). The compressed model produced by our approach is generic and does not need any special hardware/software support. Moreover, augmenting with other methods such as knowledge distillation, quantization, and connection pruning can increase the degree of compression for the proposed approach. We show the efficacy of our proposed approach for classification and object detection tasks.

### Auto Deep Compression by Reinforcement Learning Based Actor-Critic Structure

Model-based compression is an effective, facilitating, and expanded model of neural network models with limited computing and low power. However, conventional models of compression techniques utilize crafted features [2,3,12] and explore specialized areas for exploration and design of large spaces in terms of size, speed, and accuracy, which usually have returns Less and time is up. This paper will effectively analyze deep auto compression (ADC) and reinforcement learning strength in an effective sample and space design, and improve the compression quality of the model. The results of compression of the advanced model are obtained without any human effort and in a completely automated way. With a 4- fold reduction in FLOP, the accuracy of 2.8% is higher than the manual compression model for VGG-16 in ImageNet.

### Effective Network Compression Using Simulation-Guided Iterative Pruning

Existing high-performance deep learning models require very intensive computing. For this reason, it is difficult to embed a deep learning model into a system with limited resources. In this paper, we propose the novel idea of the network compression as a method to solve this limitation. The principle of this idea is to make iterative pruning more effective and sophisticated by simulating the reduced network. A simple experiment was conducted to evaluate the method; the results showed that the proposed method achieved higher performance than existing methods at the same pruning level.

### Few Shot Network Compression via Cross Distillation

Model compression has been widely adopted to obtain light-weighted deep neural networks. Most prevalent methods, however, require fine-tuning with sufficient training data to ensure accuracy, which could be challenged by privacy and security issues. As a compromise between privacy and performance, in this paper we investigate few shot network compression: given few samples per class, how can we effectively compress the network with negligible performance drop? The core challenge of few shot network compression lies in high estimation errors from the original network during inference, since the compressed network can easily over-fits on the few training instances. The estimation errors could propagate and accumulate layer-wisely and finally deteriorate the network output. To address the problem, we propose cross distillation, a novel layer-wise knowledge distillation approach. By interweaving hidden layers of teacher and student network, layer-wisely accumulated estimation errors can be effectively reduced. The proposed method offers a general framework compatible with prevalent network compression techniques such as pruning. Extensive experiments on benchmark datasets demonstrate that cross distillation can significantly improve the student network's accuracy when only a few training instances are available.