Grad-CAM++
PulseAugur coverage of Grad-CAM++ — every cluster mentioning Grad-CAM++ across labs, papers, and developer communities, ranked by signal.
- used by alphaXiv 70%
- used by Gotit.pub 70%
- used by ScienceCast 70%
- used by ResNet-50 70%
- uses Monte Carlo Dropout 70%
- used by Monte Carlo Dropout 70%
- used by Inceptionv3 70%
- used by VGG19 70%
- used by Local Interpretable Model-Agnostic Explanations for Classification of Lymph Node Metastases 70%
- used by ResNet18 70%
- used by CatalyzeX 60%
- uses Convolutional Block Attention Module 60%
11 day(s) with sentiment data
-
New Evidential Deep Learning framework enhances glaucoma detection uncertainty
Researchers have developed a new Evidential Deep Learning (EDL) framework that incorporates conditional entropy to better quantify predictive uncertainty in glaucoma detection from retinal images. This novel approach de…
-
Street View Imagery Pipeline Enhances Vacant House Assessment Accuracy
Researchers have developed a street view imagery processing pipeline to improve the accuracy of vacant house assessments in South Korea. This pipeline explicitly filters images for task suitability and identifies the ta…
-
Federated learning system FedXCrop enhances privacy in crop disease detection
Researchers have developed FedXCrop, a federated learning system designed for privacy-preserving crop disease detection. This system allows for the training of disease classification models without farms needing to shar…
-
Saliency maps in mammography classifiers show bias against dense breasts
A new research paper published on arXiv investigates the fairness of saliency maps in mammography classifiers, specifically focusing on dense breasts where cancers can be more difficult to detect. The study found that s…
-
New CheXPoT Framework Improves VLM Radiology Report Accuracy
Researchers have developed CheXPoT, a post-hoc framework designed to verify and correct radiology reports generated by vision-language models (VLMs). This program-of-thought (PoT) system decomposes reports, grounds find…
-
AI models evaluated for IoT DDoS detection under resource constraints
A new research paper evaluates several pre-trained convolutional neural network architectures for detecting distributed denial-of-service (DDoS) attacks in resource-constrained Internet of Things (IoT) environments. The…
-
Federated training may degrade AI deepfake detection explainability
A new research paper explores whether federated training, a privacy-preserving method for training AI models, degrades the explainability of deepfake detection systems. The study found that while federated models can so…
-
Deep learning models benchmarked for breast cancer diagnosis
Researchers have evaluated ten deep learning models, including CNNs like DenseNet121 and transformer models such as ViT and Swin Transformer, for their effectiveness in diagnosing breast cancer from medical images. Dens…
-
AI model for basketball shot classification relies on visual cues, study finds
Researchers investigated the reliance of deep learning models on spurious correlations for basketball shot classification. Using Grad-CAM, they analyzed an R(2+1)D-18 model trained on the Basketball-51 dataset and found…
-
New lightweight model offers energy-efficient lung tumor segmentation
Researchers have developed a new, energy-efficient model for segmenting lung tumors, crucial for cancer detection and treatment planning. This model, based on the ENet architecture and enhanced with U-Net style skip con…
-
AI model uses explainable AI to reduce unnecessary breast biopsies
Researchers have developed a novel multimodal AI approach using Grad-CAM for explainability to predict benign breast tumors, aiming to reduce unnecessary biopsies. The system integrates a Vision Transformer for image an…
-
AI model enhances Alzheimer's detection with improved MRI interpretability
Researchers have developed a novel deep learning method called EigenGradCAM with AugSmooth to improve the accuracy and interpretability of Alzheimer's disease detection using MRI scans. This approach combines eigenvecto…
-
New framework offers interpretable singing emotion recognition
Researchers have developed a new framework for interpretable singing emotion recognition that utilizes singing-specific acoustic and vocal technique features. This approach aims to provide deeper insights into the vocal…
-
New research examines deepfake detection using AI models and human performance
Two new research papers explore the detection of deepfake images, with one paper evaluating deep learning models and the other focusing on human performance. The first study compared a model trained from scratch against…
-
CNN technosignature detector shows missed detections and false alarms
A study published on arXiv investigated the performance of a Convolutional Neural Network (CNN) designed to detect technosignatures in radio astronomy data from the Breakthrough Listen project. The research found that t…
-
AI detectors struggle to distinguish alien signals from terrestrial interference
A study published on Hugging Face highlights significant limitations in current CNN-based technosignature detectors used in the search for extraterrestrial intelligence. The research found that these detectors, when tes…
-
New cross-validation method improves AI fruit shelf-life prediction accuracy
Researchers have developed a new method for evaluating the accuracy of AI models that predict the remaining shelf life of fruit from images. Using the Hass Avocado Ripening dataset, they found that specimen-level cross-…
-
New method improves wireless split learning efficiency
Researchers have developed a new method called Importance-Aware Class-Balanced Sparsification (ICS) to address communication bottlenecks in wireless split learning. This approach allows servers to rank feature channels …
-
New deep learning benchmark for dental radiograph classification released
Researchers have developed a new deep learning benchmark for classifying periapical radiographs, addressing issues of patient-level data splits and cross-center validation. The benchmark, applied to the DentIRO dataset,…
-
Study reveals misalignment in explainable AI evaluation methods
A new study published on arXiv investigates the alignment of different evaluation strategies for explainable AI (XAI). Researchers compared subjective measures like trust and satisfaction, objective metrics such as task…