Can feature maps be used for multimodal learning (e.g., combining image and text data)?

Oct 12, 2021 ... In this paper, we propose an efficient hardware accelerator with an interlayer feature compression technique to significantly reduce the required on-chip ... Oct 25, 2021 ... ... feature maps in a CNN. Second, we develop an inference level resilience technique (ILR), which selectively reruns vulnerable inferences by ... Oct 14, 2021 ... ... CNN model using the time–frequency feature maps to classify the eight types of arrhythmias and normal sinus rhythm. The standard 12-lead ECG ... Apr 19, 2023 ... CNN visualization: deep features, attention maps. Adversarial ... Forward propagate x through the network to compute the feature map y of the ... Nov 21, 2020 ... Steps to generate feature maps:- · for layer_name, feature_map in zip(layer_names, feature_maps): · if len(feature_map.shape) == 4 k = ... Experimental results show that our proposed ACFM approach can improve a CNN's camera model identification robust- ness to resampling and JPEG compression. 2. We will explain what feature and feature map are later on. ... This creates the spatial invariance of ConvNet. Figure 7. Visualizing CNN feature maps and ... CNN Feature Maps and Object Saliency Maps. Wataru Shimoda and Keiji Yanai ... map can be obtained by up-sampling saliency maps of the intermediate ... neural network or say (S-CNN), pretrained CNN feature extractor, and hierarchical extreme learning machine ... In the top-down pathway, a subsequent feature map ... Jan 29, 2017 ... Instead of using max-pooling layers to reduce the size of the feature maps, we use convolution layers with increased strides.