Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
This paper addresses the visualisation of image classification models, learnt using deep Convolutional Networks (ConvNets). We consider two visualisation techniques, based on computing the gradient of the class score with respect to the input image. The first one generates an image, which maximises the class score [Erhan et al., 2009], thus visualising the notion of the class, captured by a ConvNet. The second technique computes a class saliency map, specific to a given image and class. We show that such maps can be employed for weakly supervised object segmentation using classification ConvNets. Finally, we establish the connection between the gradient-based ConvNet visualisation methods and deconvolutional networks [Zeiler et al., 2013].
Code (23)
Tasks
General ClassificationImage Attributionimage-classificationImage ClassificationInterpretability Techniques for Deep LearningSemantic SegmentationWeakly-Supervised Object SegmentationSimilar Papers 제목 키워드 기반
Deep Green Function Convolution for Improving Saliency in Convolutional Neural Networks
Current saliency methods require to learn large scale regional features using small convolutional kernels, which is not possible with a simple feed-forward network. Some methods solve this problem by using segmentation i…
SuperpixelsVisualising Deep Network's Time-Series Representations
Despite the popularisation of machine learning models, more often than not, they still operate as black boxes with no insight into what is happening inside the model. There exist a few methods that allow to visualise and…
Time SeriesTime Series AnalysisTime Series ClassificationTSGB: Target-Selective Gradient Backprop for Probing CNN Visual Saliency
The explanation for deep neural networks has drawn extensive attention in the deep learning community over the past few years. In this work, we study the visual saliency, a.k.a. visual explanation, to interpret convoluti…
Saliency Detection With Fully Convolutional Neural Network
Saliency detection is an important task in image processing as it can solve many problems and it usually is the first step in for other processes. Convolutional neural networks have been proved to be very effective on se…
ColorizationSaliency DetectionTrustworthy Convolutional Neural Networks: A Gradient Penalized-based Approach
Convolutional neural networks (CNNs) are commonly used for image classification. Saliency methods are examples of approaches that can be used to interpret CNNs post hoc, identifying the most relevant pixels for a predict…
General Classificationimage-classificationImage Classification