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Found 9 papers, 4 papers with code
Interpretable Online Log Analysis Using Large Language Models with Prompt Strategies
LogPrompt employs large language models (LLMs) to perform online log analysis tasks via a suite of advanced prompt strategies tailored for log tasks, which enhances LLMs' performance by up to 380. 7% compared with simple prompts.
ImDiffusion: Imputed Diffusion Models for Multivariate Time Series Anomaly Detection
To the best of our knowledge, ImDiffusion represents a pioneering approach that combines imputation-based techniques with time series anomaly detection, while introducing the novel use of diffusion models to the field.
Wise-IoU: Bounding Box Regression Loss with Dynamic Focusing Mechanism
This allows WIoU to focus on ordinary-quality anchor boxes and improve the detector's overall performance.
A Multi-task Framework for Infrared Small Target Detection and Segmentation
First, with the use of UNet as the backbone to maintain resolution and semantic information, our model can achieve a higher detection accuracy than other state-of-the-art methods by attaching a simple anchor-free head.
PRINCE: A Pruned AMP Integrated Deep CNN Method for Efficient Channel Estimation of Millimeter-wave and Terahertz Ultra-Massive MIMO Systems
Millimeter-wave (mmWave) and Terahertz (THz)-band communications exploit the abundant bandwidth to fulfill the increasing data rate demands of 6G wireless communications.
Monitoring Object Detection Abnormalities via Data-Label and Post-Algorithm Abstractions
While object detection modules are essential functionalities for any autonomous vehicle, the performance of such modules that are implemented using deep neural networks can be, in many cases, unreliable.
Training Deep Neural Networks for Wireless Sensor Networks Using Loosely and Weakly Labeled Images
Although deep learning has achieved remarkable successes over the past years, few reports have been published about applying deep neural networks to Wireless Sensor Networks (WSNs) for image targets recognition where data, energy, computation resources are limited.
Towards the Automation of Deep Image Prior
Single image inverse problem is a notoriously challenging ill-posed problem that aims to restore the original image from one of its corrupted versions.
