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Found 14 papers, 6 papers with code
Multi-stage feature decorrelation constraints for improving CNN classification performance
Considering that there are many layers in CNN, through experimental comparison and analysis, MFD Loss acts on multiple front layers of CNN, constrains the output features of each layer and each channel, and performs supervision training jointly with classification loss function during network training.
Effective Out-of-Distribution Detection in Classifier Based on PEDCC-Loss
In this method, there is no need to preprocess the input samples and the computational burden of the algorithm is reduced.
Out-of-Distribution Detection
Out of Distribution (OOD) Detection
A Softmax-free Loss Function Based on Predefined Optimal-distribution of Latent Features for Deep Learning Classifier
The loss function only restricts the latent features of the samples, including the norm-adaptive Cosine distance between the latent feature vector of the sample and the center of the predefined evenly-distributed class, and the correlation between the latent features of the samples.
Single Underwater Image Enhancement Using an Analysis-Synthesis Network
The proposed framework includes an analysis network and a synthesis network, one for priors exploration and another for priors integration.
TITA: A Two-stage Interaction and Topic-Aware Text Matching Model
To tackle this problem, we define a three-level relevance in keyword-document matching task: topic-aware relevance, partially-relevance and irrelevance.
Generation and frame characteristics of predefined evenly-distributed class centroids for pattern classification
Predefined evenly-distributed class centroids (PEDCC) can be widely used in models and algorithms of pattern classification, such as CNN classifiers, classification autoencoders, clustering, and semi-supervised learning, etc.
Risk-Constrained Thompson Sampling for CVaR Bandits
The multi-armed bandit (MAB) problem is a ubiquitous decision-making problem that exemplifies the exploration-exploitation tradeoff.
Thompson Sampling Algorithms for Mean-Variance Bandits
The multi-armed bandit (MAB) problem is a classical learning task that exemplifies the exploration-exploitation tradeoff.
Semi-supervised learning method based on predefined evenly-distributed class centroids
Meanwhile, for unlabeled samples, we also use KL divergence to constrain the consistency of the network predictions between unlabeled and augmented samples.
Incremental Classifier Learning Based on PEDCC-Loss and Cosine Distance
In this paper, we introduce an ensemble method of incremental classifier to alleviate this problem, which is based on the cosine distance between the output feature and the pre-defined center, and can let each task to be preserved in different networks.
An Image Clustering Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids and MMD Distance
The algorithm uses PEDCC (Predefined Evenly-Distributed Class Centroids) as the clustering centers, which ensures the inter-class distance of latent features is maximal, and adds data distribution constraint, data augmentation constraint, auto-encoder reconstruction constraint and Sobel smooth constraint to improve the clustering performance.
A New Loss Function for CNN Classifier Based on Pre-defined Evenly-Distributed Class Centroids
With the development of convolutional neural networks (CNNs) in recent years, the network structure has become more and more complex and varied, and has achieved very good results in pattern recognition, image classification, object detection and tracking.
A Classification Supervised Auto-Encoder Based on Predefined Evenly-Distributed Class Centroids
In this paper, a new autoencoder model - classification supervised autoencoder (CSAE) based on predefined evenly-distributed class centroids (PEDCC) is proposed.
HENet:A Highly Efficient Convolutional Neural Networks Optimized for Accuracy, Speed and Storage
In order to enhance the real-time performance of convolutional neural networks(CNNs), more and more researchers are focusing on improving the efficiency of CNN.
