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Found 57 papers, 10 papers with code
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Most real-world knowledge graphs (KG) are far from complete and comprehensive.
ClusterQ: Semantic Feature Distribution Alignment for Data-Free Quantization
To this end, we propose a new and effective data-free quantization method termed ClusterQ, which utilizes the semantic feature distribution alignment for synthetic data generation.
FCL-GAN: A Lightweight and Real-Time Baseline for Unsupervised Blind Image Deblurring
Blind image deblurring (BID) remains a challenging and significant task.
Image Harmonization by Matching Regional References
To achieve visual consistency in composite images, recent image harmonization methods typically summarize the appearance pattern of global background and apply it to the global foreground without location discrepancy.
Interactive Style Transfer: All is Your Palette
Our IST method can serve as a brush, dip style from anywhere, and then paint to any region of the target content image.
A Hybrid Physics Machine Learning Approach for Macroscopic Traffic State Estimation
Full-field traffic state information (i. e., flow, speed, and density) is critical for the successful operation of Intelligent Transportation Systems (ITS) on freeways.
Mind the Gap: Cross-Lingual Information Retrieval with Hierarchical Knowledge Enhancement
The proposed model encodes the textual information in queries, documents and the KG with multilingual BERT, and incorporates the KG information in the query-document matching process with a hierarchical information fusion mechanism.
Arbitrary Virtual Try-On Network: Characteristics Preservation and Trade-off between Body and Clothing
Based on this dataset, we then propose the Arbitrary Virtual Try-On Network (AVTON) that is utilized for all-type clothes, which can synthesize realistic try-on images by preserving and trading off characteristics of the target clothes and the reference person.
Towards Robust Knowledge Graph Embedding via Multi-task Reinforcement Learning
This will lead to low-quality and unreliable representations of KGs.
DyLex: Incorporating Dynamic Lexicons into BERT for Sequence Labeling
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.
Heterogeneous Graph Neural Network with Multi-view Representation Learning
Graph neural networks for heterogeneous graph embedding is to project nodes into a low-dimensional space by exploring the heterogeneity and semantics of the heterogeneous graph.
EGGS: Eigen-Gap Guided Search For Automated Spectral Clustering
The main idea is to find the most reliable affinity matrix among a set of candidates given by different AMC methods with different hyperparameters, where the reliability is quantified by the \textit{relative-eigen-gap} of graph Laplacian introduced in this paper.
KAISA: An Adaptive Second-Order Optimizer Framework for Deep Neural Networks
Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K-FAC's larger memory footprint hinders its applicability to large models.
Many-to-English Machine Translation Tools, Data, and Pretrained Models
While there are more than 7000 languages in the world, most translation research efforts have targeted a few high-resource languages.
Phase transition gravitational waves from pseudo-Nambu-Goldstone dark matter and two Higgs doublets
We investigate the potential stochastic gravitational waves from first-order electroweak phase transitions in a model with pseudo-Nambu-Goldstone dark matter and two Higgs doublets.
High Energy Physics - Phenomenology Cosmology and Nongalactic Astrophysics General Relativity and Quantum Cosmology
Insight-HXMT observations of Swift J0243.6+6124: the evolution of RMS pulse fractions at super-Eddington luminosity
The results show a general trend of the pulse fraction increasing with luminosity and energy at super-critical luminosity.
High Energy Astrophysical Phenomena
Dual-constrained Deep Semi-Supervised Coupled Factorization Network with Enriched Prior
Nonnegative matrix factorization is usually powerful for learning the "shallow" parts-based representation, but it clearly fails to discover deep hierarchical information within both the basis and representation spaces.
A Survey on Concept Factorization: From Shallow to Deep Representation Learning
In this paper, we therefore survey the recent advances on CF methodologies and the potential benchmarks by categorizing and summarizing the current methods.
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: Generalized Formulations
This novel approach can encode physics models, i. e., classical traffic flow models, into the Gaussian process architecture and so as to regularize the ML training process.
Convolutional Neural Network Training with Distributed K-FAC
Training neural networks with many processors can reduce time-to-solution; however, it is challenging to maintain convergence and efficiency at large scales.
Unsupervised Vehicle Re-identification with Progressive Adaptation
For PAL, a data adaptation module is employed for source domain, which generates the images with similar data distribution to unlabeled target domain as ``pseudo target samples''.
The Limit of the Batch Size
For the first time we scale the batch size on ImageNet to at least a magnitude larger than all previous work, and provide detailed studies on the performance of many state-of-the-art optimization schemes under this setting.
Bilateral Attention Network for RGB-D Salient Object Detection
To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task.
Ranked #3 on
RGB-D Salient Object Detection
on SIP
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
Gradient-Induced Co-Saliency Detection
Co-saliency detection (Co-SOD) aims to segment the common salient foreground in a group of relevant images.
Ranked #2 on
Co-Salient Object Detection
on CoCA
Macroscopic Traffic Flow Modeling with Physics Regularized Gaussian Process: A New Insight into Machine Learning Applications
To address this issue, this study presents a new modeling framework, named physics regularized machine learning (PRML), to encode classical traffic flow models (referred as physical models) into the ML architecture and to regularize the ML training process.
Semi-DerainGAN: A New Semi-supervised Single Image Deraining Network
Although supervised deep deraining networks have obtained impressive results on synthetic datasets, they still cannot obtain satisfactory results on real images due to weak generalization of rain removal capacity, i. e., the pre-trained models usually cannot handle new shapes and directions that may lead to over-derained/under-derained results.
Dense Residual Network: Enhancing Global Dense Feature Flow for Character Recognition
To construct FDRN, we propose a new fast residual dense block (f-RDB) to retain the ability of local feature fusion and local residual learning of original RDB, which can reduce the computing efforts at the same time.
Learning Hybrid Representation by Robust Dictionary Learning in Factorized Compressed Space
Specifically, J-RFDL performs the robust representation by DL in a factorized compressed space to eliminate the negative effects of noise and outliers on the results, which can also make the DL process efficient.
Convolutional Dictionary Pair Learning Network for Image Representation Learning
To address this issue, we propose a novel generalized end-to-end representation learning architecture, dubbed Convolutional Dictionary Pair Learning Network (CDPL-Net) in this paper, which integrates the learning schemes of the CNN and dictionary pair learning into a unified framework.
DerainCycleGAN: Rain Attentive CycleGAN for Single Image Deraining and Rainmaking
However, in practice it is rather common to have no un-paired images in real deraining task, in such cases how to remove the rain streaks in an unsupervised way will be a very challenging task due to lack of constraints between images and hence suffering from low-quality recovery results.
Compressed DenseNet for Lightweight Character Recognition
LDB is a convolutional block similarly as dense block, but it can reduce the computation cost and weight size to (1/L, 2/L), compared with original ones, where L is the number of layers in blocks.
Multilayer Collaborative Low-Rank Coding Network for Robust Deep Subspace Discovery
In this paper, we explore the deep multi-subspace recovery problem by designing a multilayer architecture for latent LRR.
Fully-Convolutional Intensive Feature Flow Neural Network for Text Recognition
But existing CNNs based frameworks still have several drawbacks: 1) the traditaional pooling operation may lose important feature information and is unlearnable; 2) the tradi-tional convolution operation optimizes slowly and the hierar-chical features from different layers are not fully utilized.
Deep Self-representative Concept Factorization Network for Representation Learning
In this paper, we investigate the unsupervised deep representation learning issue and technically propose a novel framework called Deep Self-representative Concept Factorization Network (DSCF-Net), for clustering deep features.
Diversifying Inference Path Selection: Moving-Mobile-Network for Landmark Recognition
We intuitively find that M$^2$Net can essentially promote the diversity of the inference path (selected blocks subset) selection, so as to enhance the recognition accuracy.
Kernelized Multiview Subspace Analysis by Self-weighted Learning
With the popularity of multimedia technology, information is always represented or transmitted from multiple views.
Discriminative Local Sparse Representation by Robust Adaptive Dictionary Pair Learning
In this paper, we propose a structured Robust Adaptive Dic-tionary Pair Learning (RA-DPL) framework for the discrim-inative sparse representation learning.
Robust Triple-Matrix-Recovery-Based Auto-Weighted Label Propagation for Classification
Our method can jointly re-cover the underlying clean data, clean labels and clean weighting spaces by decomposing the original data, predicted soft labels or weights into a clean part plus an error part by fitting noise.
Flexible Auto-weighted Local-coordinate Concept Factorization: A Robust Framework for Unsupervised Clustering
For auto-weighting, RFA-LCF jointly preserves the manifold structures in the basis concept space and new coordinate space in an adaptive manner by minimizing the reconstruction errors on clean data, anchor points and coordinates.
A Coarse-to-Fine Multi-stream Hybrid Deraining Network for Single Image Deraining
To obtain the negative rain streaks during training process more accurately, we present a new module named dual path residual dense block, i. e., Residual path and Dense path.
Learning Structured Twin-Incoherent Twin-Projective Latent Dictionary Pairs for Classification
In this setting, our TP-DPL integrates the twin-incoherence based latent flexible DPL and the joint embedding of codes as well as salient features by twin-projection into a unified model in an adaptive neighborhood-preserving manner.
Adaptive Structure-constrained Robust Latent Low-Rank Coding for Image Recovery
Specifically, AS-LRC performs the latent decomposition of given data into a low-rank reconstruction by a block-diagonal codes matrix, a group sparse locality-adaptive salient feature part and a sparse error part.
Robust Subspace Discovery by Block-diagonal Adaptive Locality-constrained Representation
Leveraging on the Frobenius-norm based latent low-rank representation model, rBDLR jointly learns the coding coefficients and salient features, and improves the results by enhancing the robustness to outliers and errors in given data, preserving local information of salient features adaptively and ensuring the block-diagonal structures of the coefficients.
Rethinking RGB-D Salient Object Detection: Models, Data Sets, and Large-Scale Benchmarks
The use of RGB-D information for salient object detection has been extensively explored in recent years.
Ranked #4 on
RGB-D Salient Object Detection
on RGBD135
RGB-D Salient Object Detection
RGB Salient Object Detection
+1
Joint Subspace Recovery and Enhanced Locality Driven Robust Flexible Discriminative Dictionary Learning
We propose a joint subspace recovery and enhanced locality based robust flexible label consistent dictionary learning method called Robust Flexible Discriminative Dictionary Learning (RFDDL).
Kernel-Induced Label Propagation by Mapping for Semi-Supervised Classification
A Kernel-Induced Label Propagation (Kernel-LP) framework by mapping is proposed for high-dimensional data classification using the most informative patterns of data in kernel space.
Jointly Learning Structured Analysis Discriminative Dictionary and Analysis Multiclass Classifier
Then we compute a linear classifier based on the approximated sparse codes by an analysis mechanism to simultaneously consider the classification and representation powers.
Robust Unsupervised Flexible Auto-weighted Local-Coordinate Concept Factorization for Image Clustering
RFA-LCF integrates the robust flexible CF, robust sparse local-coordinate coding and the adaptive reconstruction weighting learning into a unified model.
Scalable Block-Diagonal Locality-Constrained Projective Dictionary Learning
More importantly, LC-PDL avoids using the complementary data matrix to learn the sub-dictionary over each class.
Joint Label Prediction based Semi-Supervised Adaptive Concept Factorization for Robust Data Representation
To enrich prior knowledge to enhance the discrimination, RS2ACF clearly uses class information of labeled data and more importantly propagates it to unlabeled data by jointly learning an explicit label indicator for unlabeled data.
Concurrency Protocol Aiming at High Performance of Execution and Replay for Smart Contracts
Although the emergence of the programmable smart contract makes blockchain systems easily embrace a wider range of industrial areas, how to execute smart contracts efficiently becomes a big challenge nowadays.
Databases Distributed, Parallel, and Cluster Computing
Knowledge Graph Embedding with Hierarchical Relation Structure
To this end, in this paper, we extend existing KGE models TransE, TransH and DistMult, to learn knowledge representations by leveraging the information from the HRS.
FanStore: Enabling Efficient and Scalable I/O for Distributed Deep Learning
Our experiments with benchmarks and real applications show that FanStore can scale DL training to 512 compute nodes with over 90\% scaling efficiency.
Distributed, Parallel, and Cluster Computing
Human activity recognition based on time series analysis using U-Net
Traditional human activity recognition (HAR) based on time series adopts sliding window analysis method.
Robust Subspace Clustering with Compressed Data
Dimension reduction is widely regarded as an effective way for decreasing the computation, storage and communication loads of data-driven intelligent systems, leading to a growing demand for statistical methods that allow analysis (e. g., clustering) of compressed data.
ImageNet Training in Minutes
If we can make full use of the supercomputer for DNN training, we should be able to finish the 90-epoch ResNet-50 training in one minute.
The Missing Piece in Complex Analytics: Low Latency, Scalable Model Management and Serving with Velox
In this work, we present Velox, a new component of the Berkeley Data Analytics Stack.
Databases
