Convolutional Neural Networks (CNNs) have demonstrated state-of-the-art performance on many visual recognition tasks. However, the combination of convolution and pooling operations only shows invariance to small local location changes in meaningful objects in input. Sometimes, such networks are trained ...
To harness spatial homogeneity of neuroimaging data, we suggest here a new CNN architecture that combines the idea of hierarchical abstraction in CNNs with a prior on the spatial homogeneity of neuroimaging data. Whereas early layers are trained globally using standard convolutional layers, we ...
Although CNNs can only be applied to individual patches within a WSI, slide level tasks can be performed by aggregating the individual patch level results. It is, however, important to take into account how this aggregation is carried out when evaluating and comparing algorithms. Many tasks that...
Recent advances in Vision Transformer (ViT) have demonstrated its impressive performance in image classification, which makes it a promis-ing alternative to Convolutional Neural Network (CNN). Unlike CNNs, ViT represents an input image as a sequence of image patches. The patch-based input image re...
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frcnn_utils README.md Oct 7, 2020 misc_utils Added a notebook for TOG adversarial patch Mar 4, 2021 models Added a notebook for TOG adversarial patch Mar 4, 2021 pretrained_universal Provide pretrained universal perturbations for all supported models (… ...
segment boundaries better, BPR extracts and refines a series of small boundary patches along the predicted instance boundaries. The proposed BPR framework (as shown below) yields significant improvements over the Mask R-CNN baseline on the Cityscapes benchmark, especially on the boundary-aware ...
基于ViT的架构,我们主要研究ViT对补丁扰动的鲁棒性,主要考虑两种典型的扰动来比较ViT与CNN之间的鲁棒性,一个是自然腐蚀另一个是对抗攻击。令人惊讶的是,我们发现ViT并不总是比ResNet表现得更稳健。当单个图像补丁自然损坏时,与ResNet相比,ViT更健壮。然而,当输入图像补丁受到对抗性攻击时,ViT显示出比ResNet更高的漏...
Fig. 1: Workflow diagram: generation of artificial analogue datasets. Full size image Fig. 2: Deep-Channel model architecture. The input time series data were fed to the 1D Convolution layer (1D-CNN) which includes both 1D convolution layers and max pooling layers. After this, data was flatt...
Dpatch is a patch-based adversarial attack technique to fool object detection models such as YOLO and Faster-RCNN [4]. Unlike the traditional adversarial patch, Dpatch is designed in a small form that can be physically attached to objects, causing object detection models to misclassify them or ...