Search Results for author:
Found 43 papers, 5 papers with code
Safe Reinforcement Learning with Contrastive Risk Prediction
As safety violations can lead to severe consequences in real-world robotic applications, the increasing deployment of Reinforcement Learning (RL) in robotic domains has propelled the study of safe exploration for reinforcement learning (safe RL).
Exemplar Learning for Medical Image Segmentation
To address this challenging EL task, we propose an Exemplar Learning-based Synthesis Net (ELSNet) framework for medical image segmentation that enables innovative exemplar-based data synthesis, pixel-prototype based contrastive embedding learning, and pseudo-label based exploitation of the unlabeled data.
Contrastive Continual Learning with Feature Propagation
To address this shortcoming, continual machine learners are elaborated to commendably learn a stream of tasks with domain and class shifts among different tasks.
Dual GNNs: Graph Neural Network Learning with Limited Supervision
In this paper, we propose a novel Dual GNN learning framework to address this challenge task.
Generalization of Reinforcement Learning with Policy-Aware Adversarial Data Augmentation
In this work, we propose a novel policy-aware adversarial data augmentation method to augment the standard policy learning method with automatically generated trajectory data.
Selective Pseudo-Labeling with Reinforcement Learning for Semi-Supervised Domain Adaptation
Inspired by the effectiveness of pseudo-labels in domain adaptation, we propose a reinforcement learning based selective pseudo-labeling method for semi-supervised domain adaptation.
Domain Adaptation with Incomplete Target Domains
In the proposed model, we design a data imputation module to fill the missing feature values based on the partial observations in the target domain, while aligning the two domains via deep adversarial adaption.
Bi-Dimensional Feature Alignment for Cross-Domain Object Detection
Recently the problem of cross-domain object detection has started drawing attention in the computer vision community.
A Transductive Multi-Head Model for Cross-Domain Few-Shot Learning
The TMHFS method extends the Meta-Confidence Transduction (MCT) and Dense Feature-Matching Networks (DFMN) method [2] by introducing a new prediction head, i. e, an instance-wise global classification network based on semantic information, after the common feature embedding network.
Ensemble Model with Batch Spectral Regularization and Data Blending for Cross-Domain Few-Shot Learning with Unlabeled Data
In this paper, we present our proposed ensemble model with batch spectral regularization and data blending mechanisms for the Track 2 problem of the cross-domain few-shot learning (CD-FSL) challenge.
Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
In this paper, we propose a feature transformation ensemble model with batch spectral regularization for the Cross-domain few-shot learning (CD-FSL) challenge.
Multi-Level Generative Models for Partial Label Learning with Non-random Label Noise
Partial label (PL) learning tackles the problem where each training instance is associated with a set of candidate labels that include both the true label and irrelevant noise labels.
Unsupervised Domain Adaptation with Progressive Domain Augmentation
Domain adaptation aims to exploit a label-rich source domain for learning classifiers in a different label-scarce target domain.
Mutual Learning Network for Multi-Source Domain Adaptation
However, in practice the labeled data can come from multiple source domains with different distributions.
Adaptive Object Detection with Dual Multi-Label Prediction
In this paper, we propose a novel end-to-end unsupervised deep domain adaptation model for adaptive object detection by exploiting multi-label object recognition as a dual auxiliary task.
Time-aware Large Kernel Convolutions
Some of these models use all the available sequence tokens to generate an attention distribution which results in time complexity of $O(n^2)$.
Ranked #12 on
Machine Translation
on WMT2014 English-French
Adversarial Paritial Multi-label Learning
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.
Dual Adversarial Co-Learning for Multi-Domain Text Classification
In this paper we propose a novel dual adversarial co-learning approach for multi-domain text classification (MDTC).
Learning to Generate Questions with Adaptive Copying Neural Networks
Automatic question generation is an important problem in natural language processing.
Inverse Visual Question Answering with Multi-Level Attentions
In this paper, we propose a novel deep multi-level attention model to address inverse visual question answering.
Adversarial Partial Multi-Label Learning
Partial multi-label learning (PML), which tackles the problem of learning multi-label prediction models from instances with overcomplete noisy annotations, has recently started gaining attention from the research community.
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in computer vision, has received great attention in recent years.
Multi-Label Zero-Shot Learning with Transfer-Aware Label Embedding Projection
The approach projects the label embedding vectors into a low-dimensional space to induce better inter-label relationships and explicitly facilitate information transfer from seen labels to unseen labels, while simultaneously learning a max-margin multi-label classifier with the projected label embeddings.
Multi-Label Image Classification
Multi-label zero-shot learning
+1
Progressive Ensemble Networks for Zero-Shot Recognition
The ensemble network is built by learning multiple image classification functions with a shared feature extraction network but different label embedding representations, which enhance the diversity of the classifiers and facilitate information transfer to unlabeled classes.
Visual relationship detection with deep structural ranking
In this paper, we propose a novel framework, called Deep Structural Ranking, for visual relationship detection.
Deep Triplet Ranking Networks for One-Shot Recognition
Despite the breakthroughs achieved by deep learning models in conventional supervised learning scenarios, their dependence on sufficient labeled training data in each class prevents effective applications of these deep models in situations where labeled training instances for a subset of novel classes are very sparse -- in the extreme case only one instance is available for each class.
Zero-Shot Classification With Discriminative Semantic Representation Learning
The proposed approach aims to identify a set of common high-level semantic components across the two domains via non-negative sparse matrix factorization, while enforcing the representation vectors of the images in this common component-based space to be discriminatively aligned with the attribute-based label representation vectors.
Semi-Supervised Zero-Shot Classification With Label Representation Learning
Most existing zero-shot learning methods require a user to first provide a set of semantic visual attributes for each class as side information before applying a two-step prediction procedure that introduces an intermediate attribute prediction problem.
A Novel Two-Step Method for Cross Language Representation Learning
In this paper, we propose a two-step representation learning method to bridge the feature spaces of different languages by exploiting a set of parallel bilingual documents.
Robust Transfer Principal Component Analysis with Rank Constraints
Our method is based on the assumption that useful information for the recovery of a corrupted data matrix can be gained from an uncorrupted related data matrix.
Adaptive Active Learning for Image Classification
Recently active learning has attracted a lot of attention in computer vision field, as it is time and cost consuming to prepare a good set of labeled images for vision data analysis.
Active Instance Sampling via Matrix Partition
Recently, batch-mode active learning has attracted a lot of attention.
Supervised Exponential Family Principal Component Analysis via Convex Optimization
Recently, supervised dimensionality reduction has been gaining attention, owing to the realization that data labels are often available and strongly suggest important underlying structures in the data.
Discriminative Batch Mode Active Learning
Most previous studies in active learning have focused on selecting one unlabeled instance at one time while retraining in each iteration.
