cross-entropy loss function (SoftMax Inc)

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SoftMax Inc cross-entropy loss function
Cross Entropy Loss Function, supplied by SoftMax Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/cross-entropy+loss+function/cross+entropy+loss+function/pm40423252-274-20-14
Average 90 stars, based on 1 article reviews

cross-entropy loss function - by Bioz Stars, 2026-10

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Article Title: Dynamic Frequency-Decoupled Refinement Network for Polyp Segmentation.
Article Snippet: The Softmax cross-entropy loss function was used, and the model achieving the lowest validation loss during training was selected for evaluation.

Article Title: Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach.
Article Snippet: This is represented by the undermentioned equation: f j(z) = ezj Σkezk Cross-entropy loss function—SoftMax is usually paired with the cross-entropy loss function in the training phase of CNNs.

Article Title: Research on Open-Set Recognition Methods for Rolling Bearing Fault Diagnosis.
Article Snippet: Softmax uses the cross-entropy loss function (CE) for optimization, focusing on maximizing the probability distribution of known classes while neglecting the specific feature relationships between samples and the distance information between classes.

Article Title: An ultra lightweight neural network for automatic modulation classification in drone communications.
Article Snippet: A cross-entropy loss function SoftMax is employed to map the output of the fully connected layer to probabilities for each modulation category, with the label corresponding to the highest probability considered as the predicted modulation type of the input signal.

Article Title: Dynamic semantic-geometric guidance and structure transfer network for cross-scene hyperspectral image classification.
Article Snippet: Recently, cross-scene hyperspectral image classification(HSIC) via domain adaptation is drawing increasing attention.. However, most existing methods either directly align the source domain and target domain without fully mining of SD information, or perform the domain adaptation from semantic and structure aspects with simply characterization method which is sensitive to noise, resulting in the negative transfer and performance decline.. To address these issues, in this paper, we propose a novel Dynamic Semantic-Geometric Guidance and Structure Transfer (DSGG-ST) network for cross-scene hyperspectral image classification task.

Article Title: Real-Time Detection of Meningiomas by Image Segmentation: A Very Deep Transfer Learning Convolutional Neural Network Approach
Article Snippet: This is represented by the undermentioned equation: f j z = e z j Σ k e z k Cross-entropy loss function —SoftMax is usually paired with the cross-entropy loss function in the training phase of CNNs.

Article Title: Artificial Intelligence-Based Classification of CT Images Using a Hybrid SpinalZFNet.
Article Snippet: The study employed the SoftMax cross-entropy loss function, which is well-suited for addressing multiclass classification problems—quantifying the discrepancy between the output of the network and its corresponding labels.

Article Title: Optimizing CNN-YOLOv7 Models for Pepper Leaf Disease Detection and Identification
Article Snippet: Agriculture is an important source of economic growth in many emerging nations, including Ethiopia.. Pepper cultivation is essential for food security worldwide.. Peppers are subject to blight leaf disease, grey leaf spot, common rust, fruit rot, and powdery mildew.

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