What are some techniques for improving the robustness of CNNs?

Jan 30, 2023 ... Abstract. In this study, the leaves are classified by various Machine Learning (ML) and Deep Learning (DL) based Convolutional Neural ... May 12, 2022 ... Comments94 · Day 1- Live Deep Learning Community Session · Day 3- Optimizers And ANN Implementation| Live Deep Learning Community Session. Jan 23, 2023 ... Comparing the performance of various CNNs and machine learning methods in diagnosing three types of brain tumors revealed that the 2D CNN ... We believe this suggests significant avenues of future exploration for machine learning language design, pedagogy, and implementation, both inside and outside ... A convolutional neural network (CNN) is a type of feedforward neural network that learns features via filter (or kernel) optimization. This type of deep ... Nov 28, 2022 ... In this study, different deep CNN models with multiple machine learning classifiers are investigated for monkeypox disease diagnosis using ... We call this approach GRAM-CNN. To automatically label a word, this method uses the local information around a word. Therefore, the GRAM-CNN method does not ... This paper presents “EspiNet V2” a Deep Learning model, based on the region-based detector Faster R-CNN. The model is used for the detection of motorcycles ... Oct 2, 2023 ... ... machine learning techniques to overcome the non ... CNN's learned associations related to ejection events in wall-bounded turbulent flows. Sep 30, 2024 ... I'm a CS student and have already studied and implemented "normal" Neural Networks, as well as many other machine learning algorithms, so I have ...