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Improving Medical Image Classification with Label Noise Using Dual-uncertainty Estimation
Deep neural networks are known to be data-driven and label noise can hav...
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Meta Discovery: Learning to Discover Novel Classes given Very Limited Data
In learning to discover novel classes(L2DNC), we are given labeled data ...
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Understanding the Interaction of Adversarial Training with Noisy Labels
Noisy labels (NL) and adversarial examples both undermine trained models...
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Provably End-to-end Label-Noise Learning without Anchor Points
In label-noise learning, the transition matrix plays a key role in build...
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Learning Diverse-Structured Networks for Adversarial Robustness
In adversarial training (AT), the main focus has been the objective and ...
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Tackling Instance-Dependent Label Noise via a Universal Probabilistic Model
The drastic increase of data quantity often brings the severe decrease o...
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A Second-Order Approach to Learning with Instance-Dependent Label Noise
The presence of label noise often misleads the training of deep neural n...
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Extended T: Learning with Mixed Closed-set and Open-set Noisy Labels
The label noise transition matrix T, reflecting the probabilities that t...
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A Survey of Label-noise Representation Learning: Past, Present and Future
Classical machine learning implicitly assumes that labels of the trainin...
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Maximum Mean Discrepancy is Aware of Adversarial Attacks
The maximum mean discrepancy (MMD) test, as a representative two-sample ...
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Experimental Quantum Generative Adversarial Networks for Image Generation
Quantum machine learning is expected to be one of the first practical ap...
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Quantum differentially private sparse regression learning
Differentially private (DP) learning, which aims to accurately extract p...
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Weakly Supervised Temporal Action Localization with Segment-Level Labels
Temporal action localization presents a trade-off between test performan...
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Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the instance-dependent label noise is challenging, because...
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Class2Simi: A New Perspective on Learning with Label Noise
Label noise is ubiquitous in the era of big data. Deep learning algorith...
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Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
The transition matrix, denoting the transition relationship from clean l...
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Repulsive Mixture Models of Exponential Family PCA for Clustering
The mixture extension of exponential family principal component analysis...
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Quantum noise protects quantum classifiers against adversaries
Noise in quantum information processing is often viewed as a disruptive ...
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Multi-Class Classification from Noisy-Similarity-Labeled Data
A similarity label indicates whether two instances belong to the same cl...
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Towards Mixture Proportion Estimation without Irreducibility
Mixture proportion estimation (MPE) is a fundamental problem of practica...
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Confidence Scores Make Instance-dependent Label-noise Learning Possible
Learning with noisy labels has drawn a lot of attention. In this area, m...
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A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection
Deep learning-based computer vision is usually data-hungry. Many researc...
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Continuous Dropout
Dropout has been proven to be an effective algorithm for training robust...
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Where is the Bottleneck of Adversarial Learning with Unlabeled Data?
Deep neural networks (DNNs) are incredibly brittle due to adversarial ex...
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Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm
The digital retina in smart cities is to select what the City Eye tells ...
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A Quantum-inspired Algorithm for General Minimum Conical Hull Problems
A wide range of fundamental machine learning tasks that are addressed by...
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Truncated Cauchy Non-negative Matrix Factorization
Non-negative matrix factorization (NMF) minimizes the Euclidean distance...
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Are Anchor Points Really Indispensable in Label-Noise Learning?
In label-noise learning, noise transition matrix, denoting the probabili...
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Orthogonal Deep Neural Networks
In this paper, we introduce the algorithms of Orthogonal Deep Neural Net...
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DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs
Due to the high storage and search efficiency, hashing has become preval...
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dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs
Objective assessment of image quality is fundamentally important in many...
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Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain
The goal of transfer learning is to improve the performance of target le...
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Adaptive Morphological Reconstruction for Seeded Image Segmentation
Morphological reconstruction (MR) is often employed by seeded image segm...
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Multi-View Matrix Completion for Multi-Label Image Classification
There is growing interest in multi-label image classification due to its...
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Decomposition-Based Transfer Distance Metric Learning for Image Classification
Distance metric learning (DML) is a critical factor for image analysis a...
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Fast Supervised Discrete Hashing
Learning-based hashing algorithms are "hot topics" because they can grea...
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Supervised Discrete Hashing with Relaxation
Data-dependent hashing has recently attracted attention due to being abl...
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A Regularization Approach for Instance-Based Superset Label Learning
Different from the traditional supervised learning in which each trainin...
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On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning
Multitask learning (MTL) aims to learn multiple tasks simultaneously thr...
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Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods for vision applicatio...
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Why ResNet Works? Residuals Generalize
Residual connections significantly boost the performance of deep neural ...
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Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcra...
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An Optimal Transport View on Generalization
We derive upper bounds on the generalization error of learning algorithm...
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The Expressive Power of Parameterized Quantum Circuits
Parameterized quantum circuits (PQCs) have been broadly used as a hybrid...
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Implementable Quantum Classifier for Nonlinear Data
In this Letter, we propose a quantum machine learning scheme for the cla...
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Instance-Dependent PU Learning by Bayesian Optimal Relabeling
When learning from positive and unlabelled data, it is a strong assumpti...
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Domain Generalization via Conditional Invariant Representation
Domain generalization aims to apply knowledge gained from multiple label...
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An Information-Theoretic View for Deep Learning
Deep learning has transformed the computer vision, natural language proc...
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On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier
We study the rates of convergence from empirical surrogate risk minimize...
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Learning with Biased Complementary Labels
In this paper we study the classification problem in which we have acces...
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