
Learning with Group Noise
Machine learning in the context of noise is a challenging but practical ...
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Hard Example Generation by Texture Synthesis for Crossdomain Shape Similarity Learning
Imagebased 3D shape retrieval (IBSR) aims to find the corresponding 3D ...
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Deep Learning is Singular, and That's Good
In singular models, the optimal set of parameters forms an analytic set ...
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3DFUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from onl...
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Adaptive ContextAware MultiModal Network for Depth Completion
Depth completion aims to recover a dense depth map from the sparse depth...
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Partsdependent Label Noise: Towards Instancedependent Label Noise
Learning with the instancedependent 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 Labelnoise Learning
The transition matrix, denoting the transition relationship from clean l...
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MultiClass Classification from NoisySimilarityLabeled 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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Domain Adaptation As a Problem of Inference on Graphical Models
This paper is concerned with datadriven unsupervised domain adaptation,...
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LikelihoodFree Overcomplete ICA and Applications in Causal Discovery
Causal discovery witnessed significant progress over the past decades. I...
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Learning Depth from Monocular Videos Using Synthetic Data: A TemporallyConsistent Domain Adaptation Approach
Majority of stateoftheart monocular depth estimation methods are supe...
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Twin Auxiliary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past fe...
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Causal Discovery and Forecasting in Nonstationary Environments with StateSpace Models
In many scientific fields, such as economics and neuroscience, we are of...
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GeometryAware Symmetric Domain Adaptation for Monocular Depth Estimation
Supervised depth estimation has achieved high accuracy due to the advanc...
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GenerativeDiscriminative Complementary Learning
Majority of stateoftheart deep learning methods for vision applicatio...
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Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcra...
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Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge
Gliomas are the most common primary brain malignancies, with different d...
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GeometryConsistent Adversarial Networks for OneSided Unsupervised Domain Mapping
Unsupervised domain mapping aims at learning a function to translate dom...
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Domain Generalization via Conditional Invariant Representation
Domain generalization aims to apply knowledge gained from multiple label...
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Subject2Vec: GenerativeDiscriminative Approach from a Set of Image Patches to a Vector
We propose an attentionbased method that aggregates local image feature...
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MoESPNet: A MixtureofExperts Scene Parsing Network
Scene parsing is an indispensable component in understanding the semanti...
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Deep Ordinal Regression Network for Monocular Depth Estimation
Monocular depth estimation, which plays a crucial role in understanding ...
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Causal Generative Domain Adaptation Networks
We propose a new generative model for domain adaptation, in which traini...
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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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A Compromise Principle in Deep Monocular Depth Estimation
Monocular depth estimation, which plays a key role in understanding 3D s...
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Transfer Learning with Label Noise
Transfer learning aims to improve learning in the target domain with lim...
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Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Measurement error in the observed values of the variables can greatly ch...
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Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
A widely applied approach to causal inference from a nonexperimental ti...
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Mingming Gong
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