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Found 10 papers, 4 papers with code
FireMatch: A Semi-Supervised Video Fire Detection Network Based on Consistency and Distribution Alignment
Considering the limited quantity of labeled video data, we propose a semi-supervised fire detection model called FireMatch, which is based on consistency regularization and adversarial distribution alignment.
Class-Specific Distribution Alignment for Semi-Supervised Medical Image Classification
Despite the success of deep neural networks in medical image classification, the problem remains challenging as data annotation is time-consuming, and the class distribution is imbalanced due to the relative scarcity of diseases.
Image Classification
Semi-supervised Medical Image Classification
Semi-Supervised Medical Image Segmentation with Co-Distribution Alignment
Medical image segmentation has made significant progress when a large amount of labeled data are available.
dugMatting: Decomposed-Uncertainty-Guided Matting
Cutting out an object and estimating its opacity mask, known as image matting, is a key task in image and video editing.
Spatio-Temporal Context Modeling for Road Obstacle Detection
The results indicate that our method with spatio-temporal context modeling is superior to existing methods for road obstacle detection.
Deep Dual Support Vector Data Description for Anomaly Detection on Attributed Networks
In this paper, we propose an end-to-end model of Deep Dual Support Vector Data description based Autoencoder (Dual-SVDAE) for anomaly detection on attributed networks, which considers both the structure and attribute for attributed networks.
Correlation-aware Deep Generative Model for Unsupervised Anomaly Detection
Unsupervised anomaly detection aims to identify anomalous samples from highly complex and unstructured data, which is pervasive in both fundamental research and industrial applications.
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
In this paper, we propose a deep joint representation learning framework for anomaly detection through a dual autoencoder (AnomalyDAE), which captures the complex interactions between network structure and node attribute for high-quality embeddings.
Robust Classification with Sparse Representation Fusion on Diverse Data Subsets
It performs Sparse Representation Fusion based on the Diverse Subset of training samples (SRFDS), which reduces the impact of randomness of the sample set and enhances the robustness of classification results.
Joint Learning of Self-Representation and Indicator for Multi-View Image Clustering
Multi-view subspace clustering aims to divide a set of multisource data into several groups according to their underlying subspace structure.
