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Found 50 papers, 18 papers with code
DeepPaperComposer: A Simple Solution for Training Data Preparation for Parsing Research Papers
We present DeepPaperComposer, a simple solution for preparing highly accurate (100%) training data without manual labeling to extract content from scholarly articles using convolutional neural networks (CNNs).
Over-Sampling Strategy in Feature Space for Graphs based Class-imbalanced Bot Detection
Specifically, node features are first mapped to a feature space through neighborhood aggregation and then generated samples for the minority class in the feature space.
MGTAB: A Multi-Relational Graph-Based Twitter Account Detection Benchmark
However, in addition to low annotation quality, existing benchmarks generally have incomplete user relationships, suppressing graph-based account detection research.
Ranked #1 on
Stance Detection
on MGTAB
Boosting Semi-Supervised Learning with Contrastive Complementary Labeling
In this way, these data can be also very informative if we can effectively exploit these complementary labels, i. e., the classes that a sample does not belong to.
Downscaled Representation Matters: Improving Image Rescaling with Collaborative Downscaled Images
Inspired by this, we propose a Hierarchical Collaborative Downscaling (HCD) method that performs gradient descent in both HR and LR domains to improve the downscaled representations.
Text-Aware Dual Routing Network for Visual Question Answering
To address the above issues, we propose a Text-Aware Dual Routing Network (TDR) which simultaneously handles the VQA cases with and without understanding text information in the input images.
Pareto-aware Neural Architecture Generation for Diverse Computational Budgets
More critically, these independent search processes cannot share their learned knowledge (i. e., the distribution of good architectures) with each other and thus often result in limited search results.
Improving Fine-tuning of Self-supervised Models with Contrastive Initialization
Self-supervised learning (SSL) has achieved remarkable performance in pretraining the models that can be further used in downstream tasks via fine-tuning.
Towards Lightweight Super-Resolution with Dual Regression Learning
Nevertheless, it is hard for existing model compression methods to accurately identify the redundant components due to the extremely large SR mapping space.
Select and Calibrate the Low-confidence: Dual-Channel Consistency based Graph Convolutional Networks
The Graph Convolutional Networks (GCNs) have achieved excellent results in node classification tasks, but the model's performance at low label rates is still unsatisfactory.
Forex Trading Volatility Prediction using Neural Network Models
In this paper, we investigate the problem of predicting the future volatility of Forex currency pairs using the deep learning techniques.
Attribute-specific Control Units in StyleGAN for Fine-grained Image Manipulation
Image manipulation with StyleGAN has been an increasing concern in recent years. Recent works have achieved tremendous success in analyzing several semantic latent spaces to edit the attributes of the generated images. However, due to the limited semantic and spatial manipulation precision in these latent spaces, the existing endeavors are defeated in fine-grained StyleGAN image manipulation, i. e., local attribute translation. To address this issue, we discover attribute-specific control units, which consist of multiple channels of feature maps and modulation styles.
A novel multiobjective evolutionary algorithm based on decomposition and multi-reference points strategy
For performance assessment, the proposed algorithm is compared with existing four state-of-the-art multiobjective evolutionary algorithms on benchmark test problems with various types of Pareto optimal fronts.
Efficient Second-Order Optimization for Deep Learning with Kernel Machines
However, the recomputation of the Hessian matrix in the second-order optimization posts much extra computation and memory burden in the training.
Adaptive Multi-layer Contrastive Graph Neural Networks
AMC-GNN generates two graph views by data augmentation and compares different layers' output embeddings of Graph Neural Network encoders to obtain feature representations, which could be used for downstream tasks.
Indoor Localization Using Smartphone Magnetic with Multi-Scale TCN and LSTM
A novel multi-scale temporal convolutional network (TCN) and long short-term memory network (LSTM) based magnetic localization approach is proposed.
Content-Aware Convolutional Neural Networks
In practice, the convolutional operation on some of the windows (e. g., smooth windows that contain very similar pixels) can be very redundant and may introduce noises into the computation.
Caching and Computation Offloading in High Altitude Platform Station (HAPS) Assisted Intelligent Transportation Systems
In addition, the caching technique is introduced for network edges to store some of the fundamental data from the HAPS so that large propagation delays can be reduced.
Document Domain Randomization for Deep Learning Document Layout Extraction
We present document domain randomization (DDR), the first successful transfer of convolutional neural networks (CNNs) trained only on graphically rendered pseudo-paper pages to real-world document segmentation.
De-Pois: An Attack-Agnostic Defense against Data Poisoning Attacks
The results demonstrate that De-Pois is effective and efficient for detecting poisoned data against all the four types of poisoning attacks, with both the accuracy and F1-score over 0. 9 on average.
Learning Defense Transformers for Counterattacking Adversarial Examples
Relying on this, we learn a defense transformer to counterattack the adversarial examples by parameterizing the affine transformations and exploiting the boundary information of DNNs.
Digital Interference Mitigation in Space Division Multiplexing Self-Homodyne Coherent Detection
We propose a digital interference mitigation scheme to reduce the impact of mode coupling in space division multiplexing self-homodyne coherent detection and experimentally verify its effectiveness in 240-Gbps mode-multiplexed transmission over 3-mode multimode fiber.
Pareto-Frontier-aware Neural Architecture Generation for Diverse Budgets
To this end, we propose a Pareto-Frontier-aware Neural Architecture Generator (NAG) which takes an arbitrary budget as input and produces the Pareto optimal architecture for the target budget.
Calibration of manganin pressure gauge for diamond-anvil cells
However, the ruby scale can often hardly be used for programmably-controlled DAC devices, especially the piezoelectric-driving cells, where a continuous pressure calibration is required.
Applied Physics Materials Science
Towards Accurate and Compact Architectures via Neural Architecture Transformer
To address this issue, we propose a Neural Architecture Transformer++ (NAT++) method which further enlarges the set of candidate transitions to improve the performance of architecture optimization.
Pareto-Frontier-aware Neural Architecture Search
To find promising architectures under different budgets, existing methods may have to perform an independent search for each budget, which is very inefficient and unnecessary.
VIS30K: A Collection of Figures and Tables from IEEE Visualization Conference Publications
We present the VIS30K dataset, a collection of 29, 689 images that represents 30 years of figures and tables from each track of the IEEE Visualization conference series (Vis, SciVis, InfoVis, VAST).
StackRec: Efficient Training of Very Deep Sequential Recommender Models by Iterative Stacking
We validate the performance of StackRec by instantiating it with four state-of-the-art SR models in three practical scenarios with real-world datasets.
Defense-guided Transferable Adversarial Attacks
Experimentally, we show that our ASR of adversarial attack reaches to 58. 38% on average, which outperforms the state-of-the-art method by 12. 1% on the normally trained models and by 11. 13% on the adversarially trained models.
Double Forward Propagation for Memorized Batch Normalization
Although the standard BN can significantly accelerate the training of DNNs and improve the generalization performance, it has several underlying limitations which may hamper the performance in both training and inference.
Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task
The black-box nature of machine learning models hinders the deployment of some high-accuracy models in medical diagnosis.
Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps
To test GPNet, we contribute a synthetic dataset of 6-DOF object grasps; evaluation is conducted using rule-based criteria, simulation test, and real test.
Conditional Automated Channel Pruning for Deep Neural Networks
To address this issue, given a target compression rate for the whole model, one can search for the optimal compression rate for each layer.
Breaking the Curse of Space Explosion: Towards Efficient NAS with Curriculum Search
With the proposed search strategy, our Curriculum Neural Architecture Search (CNAS) method significantly improves the search efficiency and finds better architectures than existing NAS methods.
Neural encoding and interpretation for high-level visual cortices based on fMRI using image caption features
Except for deep network structure, the task or corresponding big dataset is also important for deep network models, but neglected by previous studies.
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
Extensive experiments with paired training data and unpaired real-world data demonstrate our superiority over existing methods.
BigGAN-based Bayesian reconstruction of natural images from human brain activity
In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images.
Hierarchical Neural Architecture Search for Single Image Super-Resolution
To this end, we design a hierarchical SR search space and propose a hierarchical controller for architecture search.
Human Gist Processing Augments Deep Learning Breast Cancer Risk Assessment
Radiologists can classify a mammogram as normal or abnormal at better than chance levels after less than a second's exposure to the images.
NAT: Neural Architecture Transformer for Accurate and Compact Architectures
To verify the effectiveness of the proposed strategies, we apply NAT on both hand-crafted architectures and NAS based architectures.
Investigating Task-driven Latent Feasibility for Nonconvex Image Modeling
Properly modeling latent image distributions plays an important role in a variety of image-related vision problems.
Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization
Recently, visual encoding based on functional magnetic resonance imaging (fMRI) have realized many achievements with the rapid development of deep network computation.
Cycle-Consistent Adversarial GAN: the integration of adversarial attack and defense
In image classification of deep learning, adversarial examples where inputs intended to add small magnitude perturbations may mislead deep neural networks (DNNs) to incorrect results, which means DNNs are vulnerable to them.
Auto-Embedding Generative Adversarial Networks for High Resolution Image Synthesis
To address this issue, we develop a novel GAN called Auto-Embedding Generative Adversarial Network (AEGAN), which simultaneously encodes the global structure features and captures the fine-grained details.
Category decoding of visual stimuli from human brain activity using a bidirectional recurrent neural network to simulate bidirectional information flows in human visual cortices
Despite the hierarchically similar representations of deep network and human vision, visual information flows from primary visual cortices to high visual cortices and vice versa based on the bottom-up and top-down manners, respectively.
Neurons and Cognition
Deep Learning for Image Super-resolution: A Survey
Image Super-Resolution (SR) is an important class of image processing techniques to enhance the resolution of images and videos in computer vision.
Dual Reconstruction Nets for Image Super-Resolution with Gradient Sensitive Loss
However, most deep learning methods employ feed-forward architectures, and thus the dependencies between LR and HR images are not fully exploited, leading to limited learning performance.
Accurate reconstruction of image stimuli from human fMRI based on the decoding model with capsule network architecture
We firstly employed the CapsNet to train the nonlinear mapping from image stimuli to high-level capsule features, and from high-level capsule features to image stimuli again in an end-to-end manner.
Improving Efficiency of SVM k-fold Cross-validation by Alpha Seeding
The k-fold cross-validation is commonly used to evaluate the effectiveness of SVMs with the selected hyper-parameters.
The Shallow End: Empowering Shallower Deep-Convolutional Networks through Auxiliary Outputs
As a result, the representation power of intermediate layers can be very weak and the model becomes very redundant with limited performance.
