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Mutual Information Dropout: Mutual Information Can Be All You Need
Dropout is a powerful way for preventing model overfitting. However, it is inefficient due to it randomly ignoring some neurons. Although there are...
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A high-fidelity face swap** algorithm based on mutual information-guided feature decoupling
A large number of high-quality face swap** images are often required to improve the performance of forgery detection. High-quality face swap**...
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Human pose estimation with gated multi-scale feature fusion and spatial mutual information
Although human pose estimation has achieved great success, the ambiguity of joint prediction has not been well resolved, especially in complex...
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Performance Comparison of Different HTM-Spatial Pooler Algorithms Based on Information-Theoretic Measures
Hierarchical temporal memory (HTM) is a promising unsupervised machine-learning algorithm that models key principles of neocortical computation. One...
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Improving autoencoder by mutual information maximization and shuffle attention for novelty detection
Under an open dynamic environment, a challenging task in object detection is to determine whether samples belong to a known class. Novelty detection...
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Perturbation consistency and mutual information regularization for semi-supervised semantic segmentation
Recent semi-supervised learning has attracted much attention by leveraging the hidden structures learned from unlabeled data to reduce the number of...
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Optimal sensor placement for digital twin based on mutual information and correlation with multi-fidelity data
This paper proposes an optimal sensor placement algorithm based on mutual information and correlation, which is particularly suitable for solving...
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CCOCSA-based multi-frame sparse coding super-resolution via mutual information-based weighted image fusion
Image super-resolution (SR) is one of the most urgent requirements in many applications in computer vision. Though many techniques have been proposed...
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Mutual learning generative adversarial network
It is the key to realize high fidelity image-to-image translation to realize the precise disentangling of single domain feature based on the...
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Harmonious Mutual Learning for Facial Emotion Recognition
Facial emotion recognition in the wild is an important task in computer vision, but it still remains challenging since the influence of backgrounds,...
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Mutual Information-Based Neural Network Distillation for Improving Photonic Neural Network Training
AbstractPhotonic neural networks are among the most promising recently proposed neuromorphic solutions for providing fast and energy efficient Deep...
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A Mutual Information-Based Many-Objective Optimization Method for EEG Channel Selection in the Epileptic Seizure Prediction Task
Epileptic seizure prediction using multi-channel electroencephalogram (EEG) signals is very important in clinical therapy. A large number of channels...
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Multi-match: mutual information maximization and CutEdge for semi-supervised learning
Deep supervised learning has achieved great successes in tackling complex computer vision tasks. However, it typically requires a large amount of...
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English Literature Appreciation Teaching Resources Retrieval System Based on Mutual Information Entropy
With the development of globalization and the increase of international communication, the appreciation of English literature has become an important...
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Action Representing by Constrained Conditional Mutual Information
Contrastive learning achieves a remarkable performance for representation learning by constructing the InfoNCE loss function. It enables learned...
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Maximum mutual information for feature extraction from graph-structured data: Application to Alzheimer’s disease classification
A brain network can be constructed from various imaging modalities such as magnetic resonance imaging (MRI), representing the functional or...
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CE \(^2\) : A Copula Entropic Mutual Information Estimator for Enhancing Adversarial Robustness
Deep neural networks are vulnerable to adversarial examples, which exploit imperceptible perturbations to mislead classifiers. To improve adversarial...
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Spatial dependency analysis to extract information from side-channel mixtures: extended version
Practical side-channel attacks on recent devices may be challenging due to the poor quality of acquired signals. It can originate from different...
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Asymmetric network pseudo labels mutual refinement for unsupervised domain adaptation person re-identification
In the task of unsupervised domain adaptation person re-identification, the traditional symmetric dual-branch network only generates one single...
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Two-stage structural information enhancement for source-free domain adaptation
Source-free domain adaptation (SFDA) uses models trained from source domains to solve similar tasks in unlabeled domains, without accessing source...
