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To prune, or not to prune: exploring the efficacy of pruning for model compression
Michael H. Zhu,Suyog Gupta +1 more
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In this article, the authors investigate two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning.Abstract:
Model pruning seeks to induce sparsity in a deep neural network's various connection matrices, thereby reducing the number of nonzero-valued parameters in the model. Recent reports (Han et al., 2015; Narang et al., 2017) prune deep networks at the cost of only a marginal loss in accuracy and achieve a sizable reduction in model size. This hints at the possibility that the baseline models in these experiments are perhaps severely over-parameterized at the outset and a viable alternative for model compression might be to simply reduce the number of hidden units while maintaining the model's dense connection structure, exposing a similar trade-off in model size and accuracy. We investigate these two distinct paths for model compression within the context of energy-efficient inference in resource-constrained environments and propose a new gradual pruning technique that is simple and straightforward to apply across a variety of models/datasets with minimal tuning and can be seamlessly incorporated within the training process. We compare the accuracy of large, but pruned models (large-sparse) and their smaller, but dense (small-dense) counterparts with identical memory footprint. Across a broad range of neural network architectures (deep CNNs, stacked LSTM, and seq2seq LSTM models), we find large-sparse models to consistently outperform small-dense models and achieve up to 10x reduction in number of non-zero parameters with minimal loss in accuracy.read more
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Journal ArticleDOI
Detecting and ranking pornographic content in videos
M. Borg,André Tabone,Alexia Bonnici,Stefan Daniela Cristina,Reuben A. Farrugia,Kenneth P. Camilleri +5 more
TL;DR: In this paper , a video-based pornographic detection system consisting of a convolutional neural network (CNN) for automatic feature extraction, followed by a recurrent neural network(RNN) in order to exploit the temporal information present in videos.
Proceedings ArticleDOI
FEWER: Federated Weight Recovery
TL;DR: This work proposes a novel federated learning algorithm, Federated Weight Recovery (FEWER), which enables a sparsely pruned model in the training phase, and shows that FEWER achieves higher test accuracies with less communication costs for most of the test cases.
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Training Data Poisoning in ML-CAD: Backdooring DL-Based Lithographic Hotspot Detectors
TL;DR: This work explores the threat posed by training data poisoning attacks where a malicious insider can try to insert backdoors into a deep neural network used as part of the CAD flow, and explores a potential ensemble defense against possible data contamination, showing promising attack success reduction.
Proceedings ArticleDOI
Dynamic Kernel Selection for Improved Generalization and Memory Efficiency in Meta-learning
TL;DR: Metadock is presented, a task-specific dynamic kernel selection strategy for designing compressed CNN models that generalize well on unseen tasks in meta-learning and shows that for the same inference budget, pruned versions of large CNN models obtained using this approach consistently outperform the conventional choices of CNN models.
Proceedings ArticleDOI
Continual Learning with Adversarial Training to Enhance Robustness of Image Recognition Models
TL;DR: Wang et al. as discussed by the authors developed a new defensive approach that integrates continual learning and adversarial training to improve both corruption robustness and structure compactness of the defensive model, which adopts the structure of progressive neural model to establish a robust model over time.
References
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