Search Results for author:
Found 45 papers, 20 papers with code
Learning to Detect Open Classes for Universal Domain Adaptation
The new transferability measure accurately quantifies the inclination of a target example to the open classes.
Ranked #5 on
Universal Domain Adaptation
on DomainNet
Unsupervised Transfer Learning for Spatiotemporal Predictive Networks
This paper explores a new research problem of unsupervised transfer learning across multiple spatiotemporal prediction tasks.
On Localized Discrepancy for Domain Adaptation
Finally, we further extend the localized discrepancies for achieving super transfer and derive generalization bounds that could be even more sample-efficient on source domain.
Transferable Calibration with Lower Bias and Variance in Domain Adaptation
In this paper, we delve into the open problem of Calibration in DA, which is extremely challenging due to the coexistence of domain shift and the lack of target labels.
Learning Individual Models for Imputation (Technical Report)
We propose to adaptively learn individual models over various number l of neighbors for different complete tuples.
Minimum Class Confusion for Versatile Domain Adaptation
It can be characterized as (1) a non-adversarial DA method without explicitly deploying domain alignment, enjoying faster convergence speed; (2) a versatile approach that can handle four existing scenarios: Closed-Set, Partial-Set, Multi-Source, and Multi-Target DA, outperforming the state-of-the-art methods in these scenarios, especially on one of the largest and hardest datasets to date (7. 3% on DomainNet).
Ranked #3 on
Multi-target Domain Adaptation
on DomainNet
Transferable Normalization: Towards Improving Transferability of Deep Neural Networks
Deep neural networks (DNNs) excel at learning representations when trained on large-scale datasets.
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
Before sufficient training data is available, fine-tuning neural networks pre-trained on large-scale datasets substantially outperforms training from random initialization.
Towards Understanding the Transferability of Deep Representations
3) The feasibility of transferability is related to the similarity of both input and label.
How Does Learning Rate Decay Help Modern Neural Networks?
Despite the popularity of these common beliefs, experiments suggest that they are insufficient in explaining the general effectiveness of lrDecay in training modern neural networks that are deep, wide, and nonconvex.
An Approach for Process Model Extraction By Multi-Grained Text Classification
Process model extraction (PME) is a recently emerged interdiscipline between natural language processing (NLP) and business process management (BPM), which aims to extract process models from textual descriptions.
Inpatient2Vec: Medical Representation Learning for Inpatients
In addition, for unordered medical activity set, existing medical RL methods utilize a simple pooling strategy, which would result in indistinguishable contributions among the activities for learning.
Learning to Transfer Examples for Partial Domain Adaptation
Under the condition that target labels are unknown, the key challenge of PDA is how to transfer relevant examples in the shared classes to promote positive transfer, and ignore irrelevant ones in the specific classes to mitigate negative transfer.
Ranked #4 on
Partial Domain Adaptation
on ImageNet-Caltech
Spatiotemporal Pyramid Network for Video Action Recognition
From the technical perspective, we introduce the spatiotemporal compact bilinear operator into video analysis tasks.
Deep Triplet Quantization
We propose Deep Triplet Quantization (DTQ), a novel approach to learning deep quantization models from the similarity triplets.
Ranked #1 on
Image Retrieval
on NUS-WIDE
Generalized Zero-Shot Learning with Deep Calibration Network
A technical challenge of deep learning is recognizing target classes without seen data.
Flexible Attributed Network Embedding
In this paper, we propose a novel framework, FANE, to integrate structure and property information in the network embedding process.
Multi-Task Learning of Generalizable Representations for Video Action Recognition
Rev2Net is shown to be effective on the classic action recognition task.
Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spatiotemporal Dynamics
Natural spatiotemporal processes can be highly non-stationary in many ways, e. g. the low-level non-stationarity such as spatial correlations or temporal dependencies of local pixel values; and the high-level variations such as the accumulation, deformation or dissipation of radar echoes in precipitation forecasting.
Ranked #5 on
Video Prediction
on Human3.6M
Supermassive Black Holes with High Accretion Rates in Active Galactic Nuclei. VIII. Structure of the Broad-Line Region and Mass of the Central Black Hole in Mrk 142
We apply three BLR models with different prescriptions of BLR clouds distributions and find that the best model for fitting the data of Mrk 142 is a two-zone BLR model, consistent with the theoretical BLR model surrounding slim accretion disks.
Astrophysics of Galaxies Instrumentation and Methods for Astrophysics
Multi-Adversarial Domain Adaptation
Recent advances in deep domain adaptation reveal that adversarial learning can be embedded into deep networks to learn transferable features that reduce distribution discrepancy between the source and target domains.
Ranked #24 on
Domain Adaptation
on Office-31
Deep Priority Hashing
Deep hashing enables image retrieval by end-to-end learning of deep representations and hash codes from training data with pairwise similarity information.
Cross-Modal Hamming Hashing
Extensive experiments demonstrate that CMHH can generate highly concentrated hash codes and achieve state-of-the-art cross-modal retrieval performance for both hash lookups and linear scan scenarios on three benchmark datasets, NUS-WIDE, MIRFlickr-25K, and IAPR TC-12.
Partial Adversarial Domain Adaptation
We present Partial Adversarial Domain Adaptation (PADA), which simultaneously alleviates negative transfer by down-weighing the data of outlier source classes for training both source classifier and domain adversary, and promotes positive transfer by matching the feature distributions in the shared label space.
Ranked #3 on
Partial Domain Adaptation
on DomainNet
Predcnn: Predictive learning with cascade convolutions
Predicting future frames in videos remains an unsolved but challenging problem.
Ranked #3 on
Pose Prediction
on Filtered NTU RGB+D
Deep Cauchy Hashing for Hamming Space Retrieval
Due to its computation efficiency and retrieval quality, hashing has been widely applied to approximate nearest neighbor search for large-scale image retrieval, while deep hashing further improves the retrieval quality by end-to-end representation learning and hash coding.
HashGAN: Deep Learning to Hash With Pair Conditional Wasserstein GAN
The main idea is to augment the training data with nearly real images synthesized from a new Pair Conditional Wasserstein GAN (PC-WGAN) conditioned on the pairwise similarity information.
PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning
We present PredRNN++, an improved recurrent network for video predictive learning.
Ranked #1 on
Video Prediction
on KTH
(Cond metric)
Transfer Adversarial Hashing for Hamming Space Retrieval
Existing work on deep hashing assumes that the database in the target domain is identically distributed with the training set in the source domain.
PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs
The core of this network is a new Spatiotemporal LSTM (ST-LSTM) unit that extracts and memorizes spatial and temporal representations simultaneously.
Ranked #6 on
Video Prediction
on Human3.6M
Fine-grained Pattern Matching Over Streaming Time Series
Hence, we formulate a new problem, called "fine-grained pattern matching", which allows users to specify varied granularities of matching deviation to different segments of a given pattern, and fuzzy regions for adaptive breakpoints determination between consecutive segments.
Partial Transfer Learning with Selective Adversarial Networks
Existing domain adversarial networks assume fully shared label space across domains.
Deep Visual-Semantic Quantization for Efficient Image Retrieval
This paper presents a compact coding solution with a focus on the deep learning to quantization approach, which improves retrieval quality by end-to-end representation learning and compact encoding and has already shown the superior performance over the hashing solutions for similarity retrieval.
Time Series Data Cleaning: From Anomaly Detection to Anomaly Repairing
Instead of simply discarding anomalies, we propose to (iteratively) repair them in time series data, by creatively bonding the beauty of temporal nature in anomaly detection with the widely considered minimum change principle in data repairing.
Conditional Adversarial Domain Adaptation
Adversarial learning has been embedded into deep networks to learn disentangled and transferable representations for domain adaptation.
Ranked #5 on
Domain Adaptation
on USPS-to-MNIST
HashNet: Deep Learning to Hash by Continuation
Learning to hash has been widely applied to approximate nearest neighbor search for large-scale multimedia retrieval, due to its computation efficiency and retrieval quality.
Deep Transfer Learning with Joint Adaptation Networks
Deep networks have been successfully applied to learn transferable features for adapting models from a source domain to a different target domain.
Ranked #2 on
Domain Adaptation
on HMDBfull-to-UCF
Multi-Source Unsupervised Domain Adaptation
Transfer Learning
Correlation Hashing Network for Efficient Cross-Modal Retrieval
This paper presents a Correlation Hashing Network (CHN) approach to cross-modal hashing, which jointly learns good data representation tailored to hash coding and formally controls the quantization error.
Unsupervised Domain Adaptation with Residual Transfer Networks
In this paper, we propose a new approach to domain adaptation in deep networks that can jointly learn adaptive classifiers and transferable features from labeled data in the source domain and unlabeled data in the target domain.
Learning Multiple Tasks with Multilinear Relationship Networks
Deep networks trained on large-scale data can learn transferable features to promote learning multiple tasks.
Semantics-Preserving Hashing for Cross-View Retrieval
With benefits of low storage costs and high query speeds, hashing methods are widely researched for efficiently retrieving large-scale data, which commonly contains multiple views, e. g. a news report with images, videos and texts.
Learning Transferable Features with Deep Adaptation Networks
Recent studies reveal that a deep neural network can learn transferable features which generalize well to novel tasks for domain adaptation.
Ranked #3 on
Domain Adaptation
on Synth Digits-to-SVHN
Transfer Joint Matching for Unsupervised Domain Adaptation
Visual domain adaptation, which learns an accurate classifier for a new domain using labeled images from an old domain, has shown promising value in computer vision yet still been a challenging problem.
Transfer Sparse Coding for Robust Image Representation
In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately.
Image Tag Completion via Image-Specific and Tag-Specific Linear Sparse Reconstructions
Though widely utilized for facilitating image management, user-provided image tags are usually incomplete and insufficient to describe the whole semantic content of corresponding images, resulting in performance degradations in tag-dependent applications and thus necessitating effective tag completion methods.
