Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint

• URL: http://arxiv.org/abs/2212.09062v1
• Date: Sun, 18 Dec 2022 11:02:50 GMT
• Title: Bort: Towards Explainable Neural Networks with Bounded Orthogonal Constraint
• Authors: Borui Zhang, Wenzhao Zheng, Jie Zhou, Jiwen Lu
• Abstract summary: We introduce Bort, an algorithm for improving model explainability. Based on Bort, we are able to synthesize explainable adversarial samples without additional parameters and training. We find Bort constantly improves the classification accuracy of various architectures including ResNet and DeiT on MNIST, CIFAR-10, and ImageNet.
• Score: 90.69718495533144
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning has revolutionized human society, yet the black-box nature of deep neural networks hinders further application to reliability-demanded industries. In the attempt to unpack them, many works observe or impact internal variables to improve the model's comprehensibility and transparency. However, existing methods rely on intuitive assumptions and lack mathematical guarantees. To bridge this gap, we introduce Bort, an optimizer for improving model explainability with boundedness and orthogonality constraints on model parameters, derived from the sufficient conditions of model comprehensibility and transparency. We perform reconstruction and backtracking on the model representations optimized by Bort and observe an evident improvement in model explainability. Based on Bort, we are able to synthesize explainable adversarial samples without additional parameters and training. Surprisingly, we find Bort constantly improves the classification accuracy of various architectures including ResNet and DeiT on MNIST, CIFAR-10, and ImageNet.

Related papers

• Can pruning improve certified robustness of neural networks? [106.03070538582222]
  We show that neural network pruning can improve empirical robustness of deep neural networks (NNs) Our experiments show that by appropriately pruning an NN, its certified accuracy can be boosted up to 8.2% under standard training. We additionally observe the existence of certified lottery tickets that can match both standard and certified robust accuracies of the original dense models.
  arXiv  Detail & Related papers  (2022-06-15T05:48:51Z)
• Toward Certified Robustness Against Real-World Distribution Shifts [65.66374339500025]
  We train a generative model to learn perturbations from data and define specifications with respect to the output of the learned model. A unique challenge arising from this setting is that existing verifiers cannot tightly approximate sigmoid activations. We propose a general meta-algorithm for handling sigmoid activations which leverages classical notions of counter-example-guided abstraction refinement.
  arXiv  Detail & Related papers  (2022-06-08T04:09:13Z)
• Understanding Adversarial Robustness from Feature Maps of Convolutional Layers [23.42376264664302]
  Anti-perturbation ability of a neural network mainly relies on two factors: model capacity and anti-perturbation ability. We study the anti-perturbation ability of the network from the feature maps of convolutional layers. Non-trivial improvements in terms of both natural accuracy and adversarial robustness can be achieved under various attack and defense mechanisms.
  arXiv  Detail & Related papers  (2022-02-25T00:14:59Z)
• SalFBNet: Learning Pseudo-Saliency Distribution via Feedback Convolutional Networks [8.195696498474579]
  We propose a feedback-recursive convolutional framework (SalFBNet) for saliency detection. We create a large-scale Pseudo-Saliency dataset to alleviate the problem of data deficiency in saliency detection.
  arXiv  Detail & Related papers  (2021-12-07T14:39:45Z)
• Recurrence-Aware Long-Term Cognitive Network for Explainable Pattern Classification [0.0]
  We propose an LTCN-based model for interpretable pattern classification of structured data. Our method brings its own mechanism for providing explanations by quantifying the relevance of each feature in the decision process. Our interpretable model obtains competitive performance when compared to the state-of-the-art white and black boxes.
  arXiv  Detail & Related papers  (2021-07-07T18:14:50Z)
• Sparse Flows: Pruning Continuous-depth Models [107.98191032466544]
  We show that pruning improves generalization for neural ODEs in generative modeling. We also show that pruning finds minimal and efficient neural ODE representations with up to 98% less parameters compared to the original network, without loss of accuracy.
  arXiv  Detail & Related papers  (2021-06-24T01:40:17Z)
• Gone Fishing: Neural Active Learning with Fisher Embeddings [55.08537975896764]
  There is an increasing need for active learning algorithms that are compatible with deep neural networks. This article introduces BAIT, a practical representation of tractable, and high-performing active learning algorithm for neural networks.
  arXiv  Detail & Related papers  (2021-06-17T17:26:31Z)
• Robust Implicit Networks via Non-Euclidean Contractions [63.91638306025768]
  Implicit neural networks show improved accuracy and significant reduction in memory consumption. They can suffer from ill-posedness and convergence instability. This paper provides a new framework to design well-posed and robust implicit neural networks.
  arXiv  Detail & Related papers  (2021-06-06T18:05:02Z)
• Interpolation between Residual and Non-Residual Networks [24.690238357686134]
  We present a novel ODE model by adding a damping term. It can be shown that the proposed model can recover both a ResNet and a CNN by adjusting an coefficient. Experiments on a number of image classification benchmarks show that the proposed model substantially improves the accuracy of ResNet and ResNeXt.
  arXiv  Detail & Related papers  (2020-06-10T09:36:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.