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1. Prune and Tune Ensembles: Low-Cost Ensemble Learning with Sparse Independent Subnetworks

   Whitaker, T.; Whitley, D. (January 2022, Proceedings of the AAAI Conference on Artificial Intelligence)

   na (Ed.)

   Ensemble Learning is an effective method for improving gen- eralization in machine learning. However, as state-of-the-art neural networks grow larger, the computational cost associ- ated with training several independent networks becomes ex- pensive. We introduce a fast, low-cost method for creating di- verse ensembles of neural networks without needing to train multiple models from scratch. We do this by first training a single parent network. We then create child networks by cloning the parent and dramatically pruning the parameters of each child to create an ensemble of members with unique and diverse topologies. We then briefly train each child net- work for a small number of epochs, which now converge significantly faster when compared to training from scratch. We explore various ways to maximize diversity in the child networks, including the use of anti-random pruning and one- cycle tuning. This diversity enables “Prune and Tune” ensem- bles to achieve results that are competitive with traditional ensembles at a fraction of the training cost. We benchmark our approach against state of the art low-cost ensemble meth- ods and display marked improvement in both accuracy and uncertainty estimation on CIFAR-10 and CIFAR-100. 

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2. Interpretable Diversity Analysis: Visualization Feature Representations in Low-Cost Ensembles.

   https://doi.org/10.1109/IJCNN54540.2023.10191759  

   Whitaker, T; Whitley, D. (June 2023, IEEE Internation Joint Conferenc on Neural Networks)

   Diversity is especially important for low-cost ensemble methods because members often share network structure in order to avoid training several independent models from scratch. Diversity is traditionally analyzed by measuring differences between the outputs of models. However, this gives little insight into how knowledge representations differ between ensemble members. This paper introduces several interpretability methods that can be used to qualitatively analyze diversity. We demonstrate these techniques by comparing the diversity of feature representations between child networks using two low-cost ensemble algorithms, Snapshot Ensembles and Prune and Tune Ensembles. This approach to diversity analysis can lead to valuable insights for how we measure and promote diversity in ensemble methods. 

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3. Training Your Sparse Neural Network Better with Any Mask

   Jaiswal, Ajay; Ma, Haoyu; Chen, Tianlong; Ding, Ying; Wang, Zhangyang (July 2022, International Conference on Machine Learning (ICML))

   Pruning large neural networks to create high-quality, independently trainable sparse masks, which can maintain similar performance to their dense counterparts, is very desirable due to the reduced space and time complexity. As research effort is focused on increasingly sophisticated pruning methods that leads to sparse subnetworks trainable from the scratch, we argue for an orthogonal, under-explored theme: improving training techniques for pruned sub-networks, i.e. sparse training. Apart from the popular belief that only the quality of sparse masks matters for sparse training, in this paper we demonstrate an alternative opportunity: one can carefully customize the sparse training techniques to deviate from the default dense network training protocols, consisting of introducing ``ghost" neurons and skip connections at the early stage of training, and strategically modifying the initialization as well as labels. Our new sparse training recipe is generally applicable to improving training from scratch with various sparse masks. By adopting our newly curated techniques, we demonstrate significant performance gains across various popular datasets (CIFAR-10, CIFAR-100, TinyImageNet), architectures (ResNet-18/32/104, Vgg16, MobileNet), and sparse mask options (lottery ticket, SNIP/GRASP, SynFlow, or even randomly pruning), compared to the default training protocols, especially at high sparsity levels. 

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4. Evenly Cascaded Convolutional Networks

   https://doi.org/10.1109/BigData.2018.8622196  

   Ye, Chengxi; Devaraj, Chinmaya; Maynord, Michael; Fermuller, Cornelia; Aloimonos, Yiannis (December 2018, 2018 IEEE International Conference on Big Data)

   We introduce Evenly Cascaded convolutional Network (ECN), a neural network taking inspiration from the cascade algorithm of wavelet analysis. ECN employs two feature streams - a low-level and high-level steam. At each layer these streams interact, such that low-level features are modulated using advanced perspectives from the high-level stream. ECN is evenly structured through resizing feature map dimensions by a consistent ratio, which removes the burden of ad-hoc specification of feature map dimensions. ECN produces easily interpretable features maps, a result whose intuition can be understood in the context of scale-space theory. We demonstrate that ECN's design facilitates the training process through providing easily trainable shortcuts. We report new state-of-the-art results for small networks, without the need for additional treatment such as pruning or compression - a consequence of ECN's simple structure and direct training. A 6-layered ECN design with under 500k parameters achieves 95.24% and 78.99% accuracy on CIFAR-10 and CIFAR-100 datasets, respectively, outperforming the current state-of-the-art on small parameter networks, and a 3 million parameter ECN produces results competitive to the state-of-the-art. 

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5. Surrogate Lagrangian Relaxation: A Path to Retrain-Free Deep Neural Network Pruning

   https://doi.org/10.1145/3624476  

   Zhou, Shanglin; Bragin, Mikhail A.; Gurevin, Deniz; Pepin, Lynn; Miao, Fei; Ding, Caiwen (November 2023, ACM Transactions on Design Automation of Electronic Systems)

   Network pruning is a widely used technique to reduce computation cost and model size for deep neural networks. However, the typical three-stage pipeline (i.e., training, pruning, and retraining (fine-tuning)) significantly increases the overall training time. In this article, we develop a systematic weight-pruning optimization approach based on surrogate Lagrangian relaxation (SLR), which is tailored to overcome difficulties caused by the discrete nature of the weight-pruning problem. We further prove that our method ensures fast convergence of the model compression problem, and the convergence of the SLR is accelerated by using quadratic penalties. Model parameters obtained by SLR during the training phase are much closer to their optimal values as compared to those obtained by other state-of-the-art methods. We evaluate our method on image classification tasks using CIFAR-10 and ImageNet with state-of-the-art multi-layer perceptron based networks such as MLP-Mixer; attention-based networks such as Swin Transformer; and convolutional neural network based models such as VGG-16, ResNet-18, ResNet-50, ResNet-110, and MobileNetV2. We also evaluate object detection and segmentation tasks on COCO, the KITTI benchmark, and the TuSimple lane detection dataset using a variety of models. Experimental results demonstrate that our SLR-based weight-pruning optimization approach achieves a higher compression rate than state-of-the-art methods under the same accuracy requirement and also can achieve higher accuracy under the same compression rate requirement. Under classification tasks, our SLR approach converges to the desired accuracy × faster on both of the datasets. Under object detection and segmentation tasks, SLR also converges 2× faster to the desired accuracy. Further, our SLR achieves high model accuracy even at the hardpruning stage without retraining, which reduces the traditional three-stage pruning into a two-stage process. Given a limited budget of retraining epochs, our approach quickly recovers the model’s accuracy. 

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