Open AccessProceedings Article
Selective Classification for Deep Neural Networks
Yonatan Geifman,Ran El-Yaniv +1 more
- Vol. 30, pp 4878-4887
Reads0
Chats0
TLDR
A method to construct a selective classifier given a trained neural network, which allows a user to set a desired risk level and the classifier rejects instances as needed, to grant the desired risk (with high probability).Abstract:
Selective classification techniques (also known as reject option) have not yet been considered in the context of deep neural networks (DNNs). These techniques can potentially significantly improve DNNs prediction performance by trading-off coverage. In this paper we propose a method to construct a selective classifier given a trained neural network. Our method allows a user to set a desired risk level. At test time, the classifier rejects instances as needed, to grant the desired risk (with high probability). Empirical results over CIFAR and ImageNet convincingly demonstrate the viability of our method, which opens up possibilities to operate DNNs in mission-critical applications. For example, using our method an unprecedented 2% error in top-5 ImageNet classification can be guaranteed with probability 99.9%, with almost 60% test coverage.read more
Citations
More filters
Posted Content
Leveraging Uncertainty in Deep Learning for Selective Classification
TL;DR: This study proposes a mixed-integer programming framework for classification with reject option (also known as selective classification), that investigates and combines model uncertainty and predictive mean to identify optimal classification and rejection regions.
Proceedings ArticleDOI
Risk-Controlled Selective Prediction for Regression Deep Neural Network Models
TL;DR: This paper considered the selective regression problem from a risk-coverage point of view, and proposed a method to construct a selective regression model given a trained regression DNN model and a desired regression error risk, and utilized blending variance to quantify uncertainty in regression NNs.
Proceedings ArticleDOI
Knowing the No-match: Entity Alignment with Dangling Cases
Zequn Sun,Muhao Chen,Wei Hu +2 more
TL;DR: In this article, a multi-task learning framework for both entity alignment and dangling entity detection is proposed, which can opt to abstain from predicting alignment for the detected dangling entities.
Posted Content
$\mathcal{G}$-Distillation: Reducing Overconfident Errors on Novel Samples
Zhizhong Li,Derek Hoiem +1 more
TL;DR: A simple solution that reduces overconfident errors of samples from an unknown novel distribution without increasing evaluation time is proposed: train an ensemble of classifiers and then distill into a single model using both labeled and unlabeled examples.
Posted Content
Consistent Accelerated Inference via Confident Adaptive Transformers
TL;DR: In this paper, the authors develop a novel approach for confidently accelerating inference in the large and expensive multilayer Transformers that are now ubiquitous in natural language processing (NLP) that can increase efficiency but can come with unpredictable performance costs.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Very Deep Convolutional Networks for Large-Scale Image Recognition
Karen Simonyan,Andrew Zisserman +1 more
TL;DR: In this paper, the authors investigated the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting and showed that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 layers.
Journal ArticleDOI
ImageNet Large Scale Visual Recognition Challenge
Olga Russakovsky,Jia Deng,Hao Su,Jonathan Krause,Sanjeev Satheesh,Sean Ma,Zhiheng Huang,Andrej Karpathy,Aditya Khosla,Michael S. Bernstein,Alexander C. Berg,Li Fei-Fei +11 more
TL;DR: The ImageNet Large Scale Visual Recognition Challenge (ILSVRC) as mentioned in this paper is a benchmark in object category classification and detection on hundreds of object categories and millions of images, which has been run annually from 2010 to present, attracting participation from more than fifty institutions.
Proceedings ArticleDOI
Very deep convolutional neural network based image classification using small training sample size
Shuying Liu,Weihong Deng +1 more
TL;DR: In this article, a modified VGG-16 network was used to fit CIFAR-10 without severe overfitting and achieved 8.45% error rate on the dataset.
Journal ArticleDOI
An optimum character recognition system using decision functions
TL;DR: The character recognition problem, usually resulting from characters being corrupted by printing deterioration and/or inherent noise of the devices, is considered from the viewpoint of statistical decision theory and the optimum recogition is obtained.