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Found 28 papers, 6 papers with code
How Self-Attention Improves Rare Class Performance in a Question-Answering Dialogue Agent
Contextualized language modeling using deep Transformer networks has been applied to a variety of natural language processing tasks with remarkable success.
Generative Steganographic Flow
In the forward mapping, secret data is hidden in the input latent of Glow model to generate stego images.
Synergistic Network Learning and Label Correction for Noise-robust Image Classification
Taking the expertise of DNNs to learn meaningful patterns before fitting noise, our framework first trains two networks over the current dataset with small loss selection.
Recursive Least Squares Policy Control with Echo State Network
However, limited by the strong correlation among sequential samples of the agent, ESN-based policy control algorithms are difficult to use the recursive least squares (RLS) algorithm to update the ESN's parameters.
Recursive Least Squares for Training and Pruning Convolutional Neural Networks
Convolutional neural networks (CNNs) have succeeded in many practical applications.
Stochastic Actor-Executor-Critic for Image-to-Image Translation
Training a model-free deep reinforcement learning model to solve image-to-image translation is difficult since it involves high-dimensional continuous state and action spaces.
Stochastic Planner-Actor-Critic for Unsupervised Deformable Image Registration
We evaluate our method on several 2D and 3D medical image datasets, some of which contain large deformations.
Fully Attentional Network for Semantic Segmentation
Recent non-local self-attention methods have proven to be effective in capturing long-range dependencies for semantic segmentation.
Denoised Non-Local Neural Network for Semantic Segmentation
Without any external training data, our proposed Denoised NL can achieve the state-of-the-art performance of 83. 5\% and 46. 69\% mIoU on Cityscapes and ADE20K, respectively.
Revisiting Recursive Least Squares for Training Deep Neural Networks
In this paper, to overcome these drawbacks, we propose three novel RLS optimization algorithms for training feedforward neural networks, convolutional neural networks and recurrent neural networks (including long short-term memory networks), by using the error backpropagation and our average-approximation RLS method, together with the equivalent gradients of the linear least squares loss function with respect to the linear outputs of hidden layers.
Imperceptible Adversarial Examples for Fake Image Detection
Fooling people with highly realistic fake images generated with Deepfake or GANs brings a great social disturbance to our society.
Transferable Adversarial Examples for Anchor Free Object Detection
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbation can completely change prediction result.
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing
In this paper, we propose a new model, called Attention-Augmented Network (AttaNet), to capture both global context and multilevel semantics while keeping the efficiency high.
Fast Local Attack: Generating Local Adversarial Examples for Object Detectors
The deep neural network is vulnerable to adversarial examples.
ReADS: A Rectified Attentional Double Supervised Network for Scene Text Recognition
In this paper, we elaborately design a Rectified Attentional Double Supervised Network (ReADS) for general scene text recognition.
Artificial Intelligence Distinguishes COVID-19 from Community Acquired Pneumonia on Chest CT
Materials and Methods In this retrospective and multi-center study, a deep learning model, COVID-19 detection neural network (COVNet), was developed to extract visual features from volumetric chest CT exams for the detection of COVID-19.
Category-wise Attack: Transferable Adversarial Examples for Anchor Free Object Detection
Deep neural networks have been demonstrated to be vulnerable to adversarial attacks: subtle perturbations can completely change the classification results.
Domain Embedded Multi-model Generative Adversarial Networks for Image-based Face Inpainting
We firstly represent only face regions using the latent variable as the domain knowledge and combine it with the non-face parts textures to generate high-quality face images with plausible contents.
Robust Multimodal Image Registration Using Deep Recurrent Reinforcement Learning
To this end, these two components are tackled in an end-to-end manner via reinforcement learning in this work.
ICDAR 2019 Robust Reading Challenge on Reading Chinese Text on Signboard
21 teams submit results for Task 1, 23 teams submit results for Task 2, 24 teams submit results for Task 3, and 13 teams submit results for Task 4.
DeepCenterline: a Multi-task Fully Convolutional Network for Centerline Extraction
The proposed method generates well-positioned centerlines, exhibiting lower number of missing branches and is more robust in the presence of minor imperfections of the object segmentation mask.
POI Semantic Model with a Deep Convolutional Structure
An accurate similarity calculation is challenging since the mismatch between a query and a retrieval text may exist in the case of a mistyped query or an alias inquiry.
Attention-driven Tree-structured Convolutional LSTM for High Dimensional Data Understanding
In order to address these limitations, we present tree-structured ConvLSTM models for tree-structured image analysis tasks which can be trained end-to-end.
Flow Based Self-supervised Pixel Embedding for Image Segmentation
Building on these, we demonstrate that image features can be learned in self-supervision by first training an optical flow estimator with synthetic flow data, and then learning image features from the estimated flows in real motion data.
Residual Attention based Network for Hand Bone Age Assessment
The hierarchical attention components of the residual attention subnet force our network to focus on the key components of the X-ray images and generate the final predictions as well as the associated visual supports, which is similar to the assessment procedure of clinicians.
Deep Autoencoding Gaussian Mixture Model for Unsupervised Anomaly Detection
In this paper, we present a Deep Autoencoding Gaussian Mixture Model (DAGMM) for unsupervised anomaly detection.
Risk Stratification of Lung Nodules Using 3D CNN-Based Multi-task Learning
In order to address the need for a large amount for training data for CNN, we resort to transfer learning to obtain highly discriminative features.
TumorNet: Lung Nodule Characterization Using Multi-View Convolutional Neural Network with Gaussian Process
Characterization of lung nodules as benign or malignant is one of the most important tasks in lung cancer diagnosis, staging and treatment planning.
