# Large Margin Deep Networks for Classification

@inproceedings{Elsayed2018LargeMD, title={Large Margin Deep Networks for Classification}, author={Gamaleldin F. Elsayed and Dilip Krishnan and Hossein Mobahi and Kevin Regan and Samy Bengio}, booktitle={Neural Information Processing Systems}, year={2018} }

We present a formulation of deep learning that aims at producing a large margin classifier. The notion of margin, minimum distance to a decision boundary, has served as the foundation of several theoretically profound and empirically successful results for both classification and regression tasks. However, most large margin algorithms are applicable only to shallow models with a preset feature representation; and conventional margin methods for neural networks only enforce margin at the output…

## 199 Citations

### Improved Sample Complexities for Deep Neural Networks and Robust Classification via an All-Layer Margin

- Computer ScienceICLR
- 2020

This work presents a theoretically inspired training algorithm for increasing the all-layer margin and demonstrates that the algorithm improves test performance over strong baselines in practice and obtains tighter generalization bounds for neural nets which depend on Jacobian and hidden layer norms.

### Improved Sample Complexities for Deep Networks and Robust Classification via an All-Layer Margin

- Computer ScienceArXiv
- 2019

This work analyzes a new notion of margin, which it is revealed has a clear and direct relationship with generalization for deep models, and presents a theoretically inspired training algorithm for increasing the all-layer margin.

### Deep Large-Margin Rank Loss for Multi-Label Image Classification

- Computer ScienceMathematics
- 2022

Experimental results show that the deep large-margin ranking function improves the robustness of the model in multi-label image classification tasks while enhancing the model’s anti-noise performance.

### 1-to-N Large Margin Classifier

- Computer Science2020 33rd SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI)
- 2020

This work presents a novel formulation that aims to produce generalization and noise label robustness not only by imposing a margin at the top of the neural network, but also by using the entire structure of the mini-batch data.

### Predicting the Generalization Gap in Deep Networks with Margin Distributions

- Computer ScienceICLR
- 2019

This paper proposes a measure based on the concept of margin distribution, which are the distances of training points to the decision boundary, and finds that it is necessary to use margin distributions at multiple layers of a deep network.

### Margin-Based Regularization and Selective Sampling in Deep Neural Networks

- Computer ScienceArXiv
- 2020

This work derives a new margin-based regularization formulation, termed multi-margin regularization (MMR), for deep neural networks (DNNs), and demonstrates accelerated training of DNNs by selecting samples according to a minimal margin score (MMS).

### Hold me tight! Influence of discriminative features on deep network boundaries

- Computer ScienceNeurIPS
- 2020

This work rigorously confirms that neural networks exhibit a high invariance to non-discriminative features, and shows that the decision boundaries of a DNN can only exist as long as the classifier is trained with some features that hold them together.

### Improving Adversarial Robustness of CNNs via Maximum Margin

- Computer ScienceApplied Sciences
- 2022

It is proved that the SVM auxiliary classifier can constrain the high-layer feature map of the original network to make its margin larger, thereby improving the inter-class separability and intra-class compactness of the network.

### Adversarial Margin Maximization Networks

- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2021

This paper proposes adversarial margin maximization (AMM), a learning-based regularization which exploits an adversarial perturbation as a proxy and encourages a large margin in the input space, just like the support vector machines.

### Recent Advances in Large Margin Learning

- Computer ScienceIEEE Transactions on Pattern Analysis and Machine Intelligence
- 2022

A survey of recent advances in large margin training and its theoretical foundations for (nonlinear) deep neural networks (DNNs) that are probably the most prominent machine learning models for large-scale data in the community over the past decade.

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