More is Less: A More Complicated Network with Less Inference Complexity

Dong, Xuanyi; Huang, Junshi; Yang, Yi; Yan, Shuicheng

doi:10.1109/cvpr.2017.205

Cited by 245 publications

(193 citation statements)

References 26 publications

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“…Besides filter pruning methods, we compare the acceleration of our approach with other network compression methods in Table 1. In general, different layer has different importance and sparsity [24], and the method based training [10] can automatically find it. Even though, our approach can outperform other methods.…”

Section: Comparison With Other Compression Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Online Filter Clustering and Pruning for Efficient Convnets

Zhou

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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Pruning filters is an effective method for accelerating deep neural networks (DNNs), but most existing approaches prune filters on a pre-trained network directly which limits in acceleration. Although each filter has its own effect in DNNs, but if two filters are same with each other, we could prune one safely. In this paper, we add an extra cluster loss term in the loss function which can force filters in each cluster to be similar online. After training, we keep one filter in each cluster and prune others and fine-tune the pruned network to compensate the loss. Particularly, the clusters in every layer can be defined firstly which is effective for pruning DNNs within residual blocks. Extensive experiments on CIFAR10 and CIFAR100 benchmarks demonstrate the competitive performance of our proposed filter pruning method.

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Section: Comparison With Other Compression Methodsmentioning

confidence: 99%

“…We get the baseline accuracy on CIFAR10 and CIFAR100 of 93.55% and 73.23%, respectively. (2) The ResNet-34 model replaces shortcut layer with a 1 × 1 convolutional layer of ResNet-32 [24]. The initial learning rate is set to 0.1, and is divided by 5 at each 60 epochs.…”

Section: Implementation Details and Filter Selection Criteriamentioning

confidence: 99%

Online Filter Clustering and Pruning for Efficient Convnets

Zhou

et al. 2018

2018 25th IEEE International Conference on Image Processing (ICIP)

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“…State-of-the-art techniques on CIFAR-10. We compare IADI against six state-of-the-art techniques including four dynamic skipping techniques (SkipNet [10], Block-Drop [34], SACT and ACT [8]) and two static compression techniques (PFEC [44] and LCCL [45]).…”

Section: B Performance Of the Proposed Iadimentioning

confidence: 99%

Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference

Wang

Shen

et al. 2020

IEEE J. Sel. Top. Signal Process.

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State-of-the-art convolutional neural networks (CNNs) yield record-breaking predictive performance, yet at the cost of high-energy-consumption inference, that prohibits their widely deployments in resource-constrained Internet of Things (IoT) applications. We propose a dual dynamic inference (DDI) framework that highlights the following aspects: 1) we integrate both input-dependent and resource-dependent dynamic inference mechanisms under a unified framework in order to fit the varying IoT resource requirements in practice. DDI is able to both constantly suppress unnecessary costs for easy samples, and to halt inference for all samples to meet hard resource constraints enforced; 2) we propose a flexible multigrained learning to skip (MGL2S) approach for input-dependent inference which allows simultaneous layer-wise and channelwise skipping; 3) we extend DDI to complex CNN backbones such as DenseNet and show that DDI can be applied towards optimizing any specific resource goals including inference latency or energy cost. Extensive experiments demonstrate the superior inference accuracy-resource trade-off achieved by DDI, as well as the flexibility to control such trade-offs compared to existing peer methods. Specifically, DDI can achieve up to 4 times computational savings with the same or even higher accuracy as compared to existing competitive baselines.

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“…Following works [19,31,32,33,34,35,36,37,38,39,40,41] [17] and Wasserstein GAN (WGAN) [19] to improve the perceptual quality of the output images generated by VAE and enhance the effectiveness of VAE representations for semi-supervised learning. In addition, a combination of VAE and GAN was also proposed by [42].…”

Section: Generative Adversarial Networkmentioning

confidence: 99%

Improving variational autoencoder with deep feature consistent and generative adversarial training

et al. 2019

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We present a new method for improving the performances of variational autoencoder (VAE). In addition to enforcing the deep feature consistent principle thus ensuring the VAE output and its corresponding input images to have similar deep features, we also implement a generative adversarial training mechanism to force the VAE to output realistic and natural images. We present experimental results to show that the VAE trained with our new method outperforms state of the art in generating face images with much clearer and more natural noses, eyes, teeth, hair textures as well as reasonable backgrounds. We also show that our method can learn powerful embeddings of input face images, which can be used to achieve facial attribute manipulation. Moreover we propose a multi-view feature extraction strategy to extract effective image representations, which can be used to achieve state of the art performance in facial attribute prediction.recovery [8,9,10], image privacy protection [11], unsupervised dimension reduction [12] and many other applications [13,14,15]. Deep convolutional generative models, as a branch of unsupervised learning technique in machine learning, have become an area of active research in recent years. A generative model trained with a given image database can be useful in several ways. One is to learn the essence of a dataset and generate realistic images similar to those in the dataset from random inputs. The whole dataset is "compressed" into the learned parameters of the model, which are significantly smaller than the size of the training dataset. The other is to learn reusable feature representations from unlabeled image datasets for a variety of supervised learning tasks such as image classification.In this paper, we propose a new method to train the variational autoencoder (VAE)[16] to improve its performance. In particular, we seek to improve the quality of the generated images to make them more realistic and less blurry. To achieve this, we employ objective functions based on deep feature consistent principle [17] and generative adversarial network [18,19] instead of the problematic per-pixel loss functions. The deep feature consistent can help capture important perceptual features such as spatial correlation through the learned convolutional operations, while the adversarial training helps to produce images that reside on the manifold of natural images. We also introduce several techniques to improve the convergence of GAN training in this context. In particular, instead of directly using the generated images and the real images in pixel space, the corresponding deep features extracted from pretrained networks are used to train the generator and the discriminator network. We also propose to further relax the constraint on the output of the discriminator network to balance the image reconstruction loss and the adversarial loss. We present experimental results to show that our new method can generate face images with much clearer facial parts such as eyes, nose, mouth, teeth, ears and hair textur...

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More is Less: A More Complicated Network with Less Inference Complexity

Cited by 245 publications

References 26 publications

Online Filter Clustering and Pruning for Efficient Convnets

Online Filter Clustering and Pruning for Efficient Convnets

Dual Dynamic Inference: Enabling More Efficient, Adaptive, and Controllable Deep Inference

Improving variational autoencoder with deep feature consistent and generative adversarial training

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