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1. Towards Visually Explaining Variational Autoencoders

   https://doi.org/10.1109/CVPR42600.2020.00867  

   Liu, W.; Li, R.; Zheng, M.; Karanam, S.; Wu, Z.; Bhanu, B.; Radke, R. and (January 2020, IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR))

   Recent advances in convolutional neural network (CNN) model interpretability have led to impressive progress in vi- sualizing and understanding model predictions. In partic- ular, gradient-based visual attention methods have driven much recent effort in using visual attention maps as a means for visual explanations. A key problem, however, is these methods are designed for classification and categorization tasks, and their extension to explaining generative models, e.g., variational autoencoders (VAE) is not trivial. In this work, we take a step towards bridging this crucial gap, proposing the first technique to visually explain VAEs by means of gradient-based attention. We present methods to generate visual attention from the learned latent space, and also demonstrate such attention explanations serve more than just explaining VAE predictions. We show how these attention maps can be used to localize anomalies in images, demonstrating state-of-the-art performance on the MVTec- AD dataset. We also show how they can be infused into model training, helping bootstrap the VAE into learning im- proved latent space disentanglement, demonstrated on the Dsprites dataset. 

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2. Gradient Frequency Modulation for Visually Explaining Video Understanding Models

   Lin, X; Bao, W; Wright, M; Kong, Y (November 2021, British Machine Vision Conference)

   In many applications, it is essential to understand why a machine learning model makes the decisions it does, but this is inhibited by the black-box nature of state-of-the-art neural networks. Because of this, increasing attention has been paid to explainability in deep learning, including in the area of video understanding. Due to the temporal dimension of video data, the main challenge of explaining a video action recognition model is to produce spatiotemporally consistent visual explanations, which has been ignored in the existing literature. In this paper, we propose Frequency-based Extremal Perturbation (F-EP) to explain a video understanding model's decisions. Because the explanations given by perturbation methods are noisy and non-smooth both spatially and temporally, we propose to modulate the frequencies of gradient maps from the neural network model with a Discrete Cosine Transform (DCT). We show in a range of experiments that F-EP provides more spatiotemporally consistent explanations that more faithfully represent the model's decisions compared to the existing state-of-the-art methods. 

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3. Visual Attention Prompted Prediction and Learning

   https://doi.org/10.24963/ijcai.2024/610  

   Zhang, Yifei; Pan, Bo; Gu, Siyi; Bai, Guangji; Qiu, Meikang; Yang, Xiaofeng; Zhao, Liang (August 2024, International Joint Conferences on Artificial Intelligence Organization)

   Visual explanation (attention)-guided learning uses not only labels but also explanations to guide the model reasoning process. While visual attention-guided learning has shown promising results, it requires a large number of explanation annotations that are time-consuming to prepare. However, in many real-world situations, it is usually desired to prompt the model with visual attention without model retraining. For example, when doing AI-assisted cancer classification on a medical image, users (e.g., clinicians) can provide the AI model with visual attention prompts on which areas are indispensable and which are precluded. Despite its promising objectives, achieving visual attention-prompted prediction presents several major challenges: 1) How can the visual prompt be effectively integrated into the model's reasoning process? 2) How should the model handle samples that lack visual prompts? 3) What is the impact on the model's performance when a visual prompt is imperfect? This paper introduces a novel framework for visual attention prompted prediction and learning, utilizing visual prompts to steer the model's reasoning process. To improve performance in non-prompted situations and align it with prompted scenarios, we propose a co-training approach for both non-prompted and prompted models, ensuring they share similar parameters and activation. Additionally, for instances where the visual prompt does not encompass the entire input image, we have developed innovative attention prompt refinement methods. These methods interpolate the incomplete prompts while maintaining alignment with the model's explanations. Extensive experiments on four datasets demonstrate the effectiveness of our proposed framework in enhancing predictions for samples both with and without prompt. 

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4. From local explanations to global understanding with explainable AI for trees

   https://doi.org/10.1038/s42256-019-0138-9  

   Lundberg, Scott M.; Erion, Gabriel; Chen, Hugh; DeGrave, Alex; Prutkin, Jordan M.; Nair, Bala; Katz, Ronit; Himmelfarb, Jonathan; Bansal, Nisha; Lee, Su-In (January 2020, Nature machine intelligence)

   Tree-based machine learning models such as random forests, decision trees and gradient boosted trees are popular nonlinear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here we improve the interpretability of tree-based models through three main contributions. (1) A polynomial time algorithm to compute optimal explanations based on game theory. (2) A new type of explanation that directly measures local feature interaction effects. (3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to (1) identify high-magnitude but low-frequency nonlinear mortality risk factors in the US population, (2) highlight distinct population subgroups with shared risk characteristics, (3) identify nonlinear interaction effects among risk factors for chronic kidney disease and (4) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model’s performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains. 

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5. Attention-based generative models for de novo molecular design

   https://doi.org/10.1039/D1SC01050F  

   Dollar, Orion; Joshi, Nisarg; Beck, David A.; Pfaendtner, Jim (June 2021, Chemical Science)

   null (Ed.)

   Attention mechanisms have led to many breakthroughs in sequential data modeling but have yet to be incorporated into any generative algorithms for molecular design. Here we explore the impact of adding self-attention layers to generative β -VAE models and show that those with attention are able to learn a complex “molecular grammar” while improving performance on downstream tasks such as accurately sampling from the latent space (“model memory”) or exploring novel chemistries not present in the training data. There is a notable relationship between a model's architecture, the structure of its latent memory and its performance during inference. We demonstrate that there is an unavoidable tradeoff between model exploration and validity that is a function of the complexity of the latent memory. However, novel sampling schemes may be used that optimize this tradeoff. We anticipate that attention will play an important role in future molecular design algorithms that can make efficient use of the detailed molecular substructures learned by the transformer. 

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