
-
Learning with Group Noise
Machine learning in the context of noise is a challenging but practical ...
read it
-
Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning
Image-based 3D shape retrieval (IBSR) aims to find the corresponding 3D ...
read it
-
Deep Learning is Singular, and That's Good
In singular models, the optimal set of parameters forms an analytic set ...
read it
-
3D-FUTURE: 3D Furniture shape with TextURE
The 3D CAD shapes in current 3D benchmarks are mostly collected from onl...
read it
-
Adaptive Context-Aware Multi-Modal Network for Depth Completion
Depth completion aims to recover a dense depth map from the sparse depth...
read it
-
Parts-dependent Label Noise: Towards Instance-dependent Label Noise
Learning with the instance-dependent label noise is challenging, because...
read it
-
Class2Simi: A New Perspective on Learning with Label Noise
Label noise is ubiquitous in the era of big data. Deep learning algorith...
read it
-
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning
The transition matrix, denoting the transition relationship from clean l...
read it
-
Multi-Class Classification from Noisy-Similarity-Labeled Data
A similarity label indicates whether two instances belong to the same cl...
read it
-
Towards Mixture Proportion Estimation without Irreducibility
Mixture proportion estimation (MPE) is a fundamental problem of practica...
read it
-
Domain Adaptation As a Problem of Inference on Graphical Models
This paper is concerned with data-driven unsupervised domain adaptation,...
read it
-
Likelihood-Free Overcomplete ICA and Applications in Causal Discovery
Causal discovery witnessed significant progress over the past decades. I...
read it
-
Learning Depth from Monocular Videos Using Synthetic Data: A Temporally-Consistent Domain Adaptation Approach
Majority of state-of-the-art monocular depth estimation methods are supe...
read it
-
Twin Auxiliary Classifiers GAN
Conditional generative models enjoy remarkable progress over the past fe...
read it
-
Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models
In many scientific fields, such as economics and neuroscience, we are of...
read it
-
Geometry-Aware Symmetric Domain Adaptation for Monocular Depth Estimation
Supervised depth estimation has achieved high accuracy due to the advanc...
read it
-
Generative-Discriminative Complementary Learning
Majority of state-of-the-art deep learning methods for vision applicatio...
read it
-
Robust Angular Local Descriptor Learning
In recent years, the learned local descriptors have outperformed handcra...
read it
-
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...
read it
-
Geometry-Consistent Adversarial Networks for One-Sided Unsupervised Domain Mapping
Unsupervised domain mapping aims at learning a function to translate dom...
read it
-
Domain Generalization via Conditional Invariant Representation
Domain generalization aims to apply knowledge gained from multiple label...
read it
-
Subject2Vec: Generative-Discriminative Approach from a Set of Image Patches to a Vector
We propose an attention-based method that aggregates local image feature...
read it
-
MoE-SPNet: A Mixture-of-Experts Scene Parsing Network
Scene parsing is an indispensable component in understanding the semanti...
read it
-
Deep Ordinal Regression Network for Monocular Depth Estimation
Monocular depth estimation, which plays a crucial role in understanding ...
read it
-
Causal Generative Domain Adaptation Networks
We propose a new generative model for domain adaptation, in which traini...
read it
-
Learning with Biased Complementary Labels
In this paper we study the classification problem in which we have acces...
read it
-
A Compromise Principle in Deep Monocular Depth Estimation
Monocular depth estimation, which plays a key role in understanding 3D s...
read it
-
Transfer Learning with Label Noise
Transfer learning aims to improve learning in the target domain with lim...
read it
-
Causal Discovery in the Presence of Measurement Error: Identifiability Conditions
Measurement error in the observed values of the variables can greatly ch...
read it
-
Causal Inference by Identification of Vector Autoregressive Processes with Hidden Components
A widely applied approach to causal inference from a non-experimental ti...
read it