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09/18/2023
Efficient Low-Rank GNN Defense Against Structural Attacks
Graph Neural Networks (GNNs) have been shown to possess strong represent...
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09/18/2023
GDM: Dual Mixup for Graph Classification with Limited Supervision
Graph Neural Networks (GNNs) require a large number of labeled graph sam...
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09/04/2023
Adaptive Parametric Prototype Learning for Cross-Domain Few-Shot Classification
Cross-domain few-shot classification induces a much more challenging pro...
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05/03/2023
Evolving Dictionary Representation for Few-shot Class-incremental Learning
New objects are continuously emerging in the dynamically changing world ...
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12/17/2022
Annotation by Clicks: A Point-Supervised Contrastive Variance Method for Medical Semantic Segmentation
Medical image segmentation methods typically rely on numerous dense anno...
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12/15/2022
Dual Moving Average Pseudo-Labeling for Source-Free Inductive Domain Adaptation
Unsupervised domain adaptation reduces the reliance on data annotation i...
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09/10/2022
Safe Reinforcement Learning with Contrastive Risk Prediction
As safety violations can lead to severe consequences in real-world robot...
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04/03/2022
Exemplar Learning for Medical Image Segmentation
Medical image annotation typically requires expert knowledge and hence i...
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12/03/2021
Contrastive Continual Learning with Feature Propagation
Classical machine learners are designed only to tackle one task without ...
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06/29/2021
Dual GNNs: Graph Neural Network Learning with Limited Supervision
Graph Neural Networks (GNNs) require a relatively large number of labele...
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06/29/2021
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation
The generalization gap in reinforcement learning (RL) has been a signifi...
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12/07/2020
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation
Recent domain adaptation methods have demonstrated impressive improvemen...
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12/03/2020
Domain Adaptation with Incomplete Target Domains
Domain adaptation, as a task of reducing the annotation cost in a target...
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11/14/2020
Bi-Dimensional Feature Alignment for Cross-Domain Object Detection
Recently the problem of cross-domain object detection has started drawin...
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06/08/2020
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
Deep learning models are difficult to obtain good performance when data ...
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05/18/2020
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
Deep learning models often require much annotated data to obtain good pe...
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05/11/2020
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise
Partial label (PL) learning tackles the problem where each training inst...
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04/03/2020
Unsupervised Domain Adaptation with Progressive Domain Augmentation
Domain adaptation aims to exploit a label-rich source domain for learnin...
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03/29/2020
Mutual Learning Network for Multi-Source Domain Adaptation
Early Unsupervised Domain Adaptation (UDA) methods have mostly assumed t...
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03/29/2020
Adaptive Object Detection with Dual Multi-Label Prediction
In this paper, we propose a novel end-to-end unsupervised deep domain ad...
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02/08/2020
Time-aware Large Kernel Convolutions
To date, most state-of-the-art sequence modelling architectures use atte...
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09/18/2019
Dual Adversarial Co-Learning for Multi-Domain Text Classification
In this paper we propose a novel dual adversarial co-learning approach f...
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09/17/2019
Learning to Generate Questions with Adaptive Copying Neural Networks
Automatic question generation is an important problem in natural languag...
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09/17/2019
Inverse Visual Question Answering with Multi-Level Attentions
In this paper, we propose a novel deep multi-level attention model to ad...
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09/15/2019
Adversarial Partial Multi-Label Learning
Partial multi-label learning (PML), which tackles the problem of learnin...
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05/13/2019
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problem...
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08/07/2018
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
Zero-shot learning transfers knowledge from seen classes to novel unseen...
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05/18/2018
Self-Training Ensemble Networks for Zero-Shot Image Recognition
Despite the advancement of supervised image recognition algorithms, thei...
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04/19/2018
Deep Triplet Ranking Networks for One-Shot Recognition
Despite the breakthroughs achieved by deep learning models in convention...
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07/04/2012
Maximum Margin Bayesian Networks
We consider the problem of learning Bayesian network classifiers that ma...
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06/27/2012
