Survey on Leveraging Uncertainty Estimation Towards Trustworthy Deep Neural Networks: The Case of Reject Option and Post-training Processing

Although neural networks (especially deep neural networks) have achieved \textit{better-than-human} performance in many fields, their real-world deployment is still questionable due to the lack of awareness about the limitation in their knowledge. To incorporate such awareness in the machine learning model, prediction with reject option (also known as selective classification or classification with abstention) has been proposed in literature. In this paper, we present a systematic review of the prediction with the reject option in the context of various neural networks. To the best of our knowledge, this is the first study focusing on this aspect of neural networks. Moreover, we discuss different novel loss functions related to the reject option and post-training processing (if any) of network output for generating suitable measurements for knowledge awareness of the model. Finally, we address the application of the rejection option in reducing the prediction time for the real-time problems and present a comprehensive summary of the techniques related to the reject option in the context of extensive variety of neural networks. Our code is available on GitHub: \url{https://github.com/MehediHasanTutul/Reject_option

Borrowing Treasures from the Wealthy: Deep Transfer Learning through Selective Joint Fine-tuning

Deep neural networks require a large amount of labeled training data during supervised learning. However, collecting and labeling so much data might be infeasible in many cases. In this paper, we introduce a source-target selective joint fine-tuning scheme for improving the performance of deep learning tasks with insufficient training data. In this scheme, a target learning task with insufficient training data is carried out simultaneously with another source learning task with abundant training data. However, the source learning task does not use all existing training data. Our core idea is to identify and use a subset of training images from the original source learning task whose low-level characteristics are similar to those from the target learning task, and jointly fine-tune shared convolutional layers for both tasks. Specifically, we compute descriptors from linear or nonlinear filter bank responses on training images from both tasks, and use such descriptors to search for a desired subset of training samples for the source learning task. Experiments demonstrate that our selective joint fine-tuning scheme achieves state-of-the-art performance on multiple visual classification tasks with insufficient training data for deep learning. Such tasks include Caltech 256, MIT Indoor 67, Oxford Flowers 102 and Stanford Dogs 120. In comparison to fine-tuning without a source domain, the proposed method can improve the classification accuracy by 2% - 10% using a single model.Comment: To appear in 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2017

The Possibility of Selective Skin Lesion Classification in Convolutional Neural Networks

Selective classification of skin lesion images and uncertainty estimation is examined to increase the adoption of convolutional neural networks(CNNs) in automated skin cancer diagnostic systems. Research on the application of deep learning models to skin cancer diagnosis has shown success as models outperform medical experts [1]. However, concerns on uncertainty in classifiers and difficulty in approximating uncertainty has caused limited adoption of CNNs in Computer-aided diagnostic systems (CADs) in health care. This research propose selective classification to increase confidence in CNN models for skin cancer diagnosis. The methodology is based on SoftMax response(SR), MC dropout and risk-coverage performance evaluation metric.  Risk-coverage curves gives physicians and dermatologist information about the expected rate of misclassification by a model. This enable them to measure the reliability of the classifier’s predictions and inform their decision during skin cancer diagnosis. MC dropout uncertainty estimate was shown to increase accuracy for Melanoma detection by 1.48%. The proposed selective classifier achieved increase melanoma detection. The sensitivity of melanoma increased by 9.91% and 9.73% after selective classification at a coverage of 0.7. This study showed that selective classification and uncertainty estimation can be combined to promote adoption of CNNs in CADs for skin lesions classification

Are there any ‘object detectors’ in the hidden layers of CNNs trained to identify objects or scenes?

Various methods of measuring unit selectivity have been developed with the aim of better understanding how neural networks work. But the different measures provide divergent estimates of selectivity, and this has led to different conclusions regarding the conditions in which selective object representations are learned and the functional relevance of these representations. In an attempt to better characterize object selectivity, we undertake a comparison of various selectivity measures on a large set of units in AlexNet, including localist selectivity, precision, class-conditional mean activity selectivity (CCMAS), network dissection,the human interpretation of activation maximization (AM) images, and standard signal-detection measures. We find that the different measures provide different estimates of object selectivity, with precision and CCMAS measures providing misleadingly high estimates. Indeed, the most selective units had a poor hit-rate or a high false-alarm rate (or both) in object classification, making them poor object detectors. We fail to find any units that are even remotely as selective as the 'grandmother cell' units reported in recurrent neural networks. In order to generalize these results, we compared selectivity measures on units in VGG-16 and GoogLeNet trained on the ImageNet or Places-365 datasets that have been described as 'object detectors'. Again, we find poor hit-rates and high false-alarm rates for object classification. We conclude that signal-detection measures provide a better assessment of single-unit selectivity compared to common alternative approaches, and that deep convolutional networks of image classification do not learn object detectors in their hidden layers.Comment: Published in Vision Research 2020, 19 pages, 8 figure

Selective deep convolutional neural network for low cost distorted image classification

Neural networks trained using images with a certain type of distortion should be better at classifying test images with the same type of distortion than generally-trained neural networks, given other factors being equal. Based on this observation, an ensemble of convolutional neural networks (CNNs) trained with different types and degrees of distortions is used. However, instead of simply classifying test images of unknown distortion types with the entire ensemble of CNNs, an extra tiny CNN is specifically trained to distinguish between the different types and degrees of distortions. Then, only the dedicated CNN for that specific type and degree of distortion, as determined by the tiny CNN, is activated and used to classify a possibly distorted test image. This proposed architecture, referred to as a \textit{selective deep convolutional neural network (DCNN)}, is implemented and found to result in high accuracy with low hardware costs. Detailed simulations with realistic image distortion scenarios using three popular datasets show that memory, MAC operations, and energy savings of up to 93.68%, 93.61%, and 91.92%, respectively, can be achieved with almost no reduction in image classification accuracy. The proposed selective DCNN scores up to 2.18x higher than the state-of-the-art DCNN model when evaluated using NetScore, a comprehensive metric that considers both CNN performance and hardware cost. In addition, it is shown that even higher hardware cost reduction can be achieved when selective DCNN is combined with previously proposed model compression techniques. Finally, experiments conducted with extended types and degrees of image distortion show that selective DCNN is highly scalable.11Ysciescopu