Deep fair models for complex data: Graphs labeling and explainable face recognition

Explainable AI (XAI) is an active research area to interpret a neural network’s decision by ensuring transparency and trust in the task-specified learned models. Recently, perturbation-based model analysis has shown better interpretation, but backpropagation techniques are still prevailing because of their computational efficiency. In this work, we combine both approaches as a hybrid visual explanation algorithm and propose an efficient interpretation method for convolutional neural networks. Our method adaptively selects the most critical features that mainly contribute towards a prediction to probe the model by finding the activated features. Experimental results show that the proposed method can reduce the execution time up to 30% while enhancing competitive interpretability without compromising the quality of explanation generated.

Ada-Sise: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks

The evolution of Deep Learning (DL), a subset of machine learning, has made their use very 6 effective in many artificial intelligence (AI) fields. In parallel Virtual Reality is going wide in 7 many applications thanks to the proliferation of cameras in mobile devices and improved 8 processing eﬀiciency. Data visualization in deep learning is a fundamental element for which it 9 can benefit from the advantages offered by the visualization of the VR for the development of 10 the models. In addition, the researchers can widely use the editing of images and videos in the 11 machine learning process to design a convolutional network suitable for image recognition. In 12 this study, we want to demonstrate the usefulness of this approach in collecting data within 13 virtual reality to train and optimize a convolutional neural network used to recognize human 14 activities (HAR).

Neural Network Design using a Virtual Reality Platform 1

Video Anomaly Event Detection (VAED) is the core technology of intelligent surveillance systems aiming to temporally or spatially locate anomalous events in videos. With the penetration of deep learning, the recent advances in VAED have diverged various routes and achieved significant success. However, most existing reviews focus on traditional and unsupervised VAED methods, lacking attention to emerging weakly-supervised and fully-unsupervised routes. Therefore, this review extends the narrow VAED concept from unsupervised video anomaly detection to Generalized Video Anomaly Event Detection (GVAED), which provides a comprehensive survey that integrates recent works based on different assumptions and learning frameworks into an intuitive taxonomy and coordinates unsupervised, weakly-supervised, fully-unsupervised, and supervised VAED routes. To facilitate future researchers, this review collates and releases research resources such as datasets, available codes, programming tools, and literature. Moreover, this review quantitatively compares the model performance and analyzes the research challenges and possible trends for future work.

Generalized Video Anomaly Event Detection: Systematic Taxonomy and Comparison of Deep Models

Survey of explainable artificial intelligence techniques for biomedical imaging with deep neural networks

Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image recognition applications. Grad-CAM is a popular solution that provides such a visualization by combining the activation maps obtained from the model. However, the average gradient-based terms deployed in this method underestimates the contribution of the representations discovered by the model to its predictions. Addressing this problem, we introduce a solution to tackle this issue by computing the path integral of the gradient-based terms in Grad-CAM. We conduct a thorough analysis to demonstrate the improvement achieved by our method in measuring the importance of the extracted representations for the CNN’s predictions, which yields to our method’s administration in object localization and model interpretation.

Integrated Grad-Cam: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks Via Integrated Gradient-Based Scoring

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide lower resolution and blurry explanation maps that limit their explanation power. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

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SVEA: A Small-Scale Benchmark for Validating the Usability of Post-Hoc Explainable AI Solutions in Image and Signal Recognition

Visualizing the features captured by Convolutional Neural Networks (CNNs) is one of the conventional approaches to interpret the predictions made by these models in numerous image recognition applications. Grad-CAM is a popular solution that provides such a visualization by combining the activation maps obtained from the model. However, the average gradient-based terms deployed in this method underestimates the contribution of the representations discovered by the model to its predictions. Addressing this problem, we introduce a solution to tackle this issue by computing the path integral of the gradient-based terms in Grad-CAM. We conduct a thorough analysis to demonstrate the improvement achieved by our method in measuring the importance of the extracted representations for the CNN's predictions, which yields to our method's administration in object localization and model interpretation.

Mahesh Sudhakar

Papers

Integrated Grad-Cam: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks Via Integrated Gradient-Based Scoring

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

Ada-Sise: Adaptive Semantic Input Sampling for Efficient Explanation of Convolutional Neural Networks

SVEA: A Small-Scale Benchmark for Validating the Usability of Post-Hoc Explainable AI Solutions in Image and Signal Recognition

Integrated Grad-CAM: Sensitivity-Aware Visual Explanation of Deep Convolutional Networks via Integrated Gradient-Based Scoring