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Found 69 papers, 18 papers with code
Dual Adversarial Network for Deep Active Learning
In this paper, we investigate the overlapping problem of recent uncertainty-based approaches and propose to alleviate the issue by taking representativeness into consideration.
Large-Scale Few-Shot Learning via Multi-Modal Knowledge Discovery
It is a challenging problem since (1) the identifying process is susceptible to over-fitting with limited samples of an object, and (2) the sample imbalance between a base (known knowledge) category and a novel category is easy to bias the recognition results.
Exclusivity-Consistency Regularized Knowledge Distillation for Face Recognition
Knowledge distillation is an effective tool to compress large pre-trained Convolutional Neural Networks (CNNs) or their ensembles into models applicable to mobile and embedded devices.
MSP: Multi-Stage Prompting for Making Pre-trained Language Models Better Translators
During each stage, we independently apply different continuous prompts for allowing pre-trained language models better adapting to translation tasks.
Enhancing MR Image Segmentation with Realistic Adversarial Data Augmentation
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training.
Joint Semi-supervised 3D Super-Resolution and Segmentation with Mixed Adversarial Gaussian Domain Adaptation
Optimising the analysis of cardiac structure and function requires accurate 3D representations of shape and motion.
Joint Motion Correction and Super Resolution for Cardiac Segmentation via Latent Optimisation
In cardiac magnetic resonance (CMR) imaging, a 3D high-resolution segmentation of the heart is essential for detailed description of its anatomical structures.
Language Models are Good Translators
Inspired by the recent progress of large-scale pre-trained language models on machine translation in a limited scenario, we firstly demonstrate that a single language model (LM4MT) can achieve comparable performance with strong encoder-decoder NMT models on standard machine translation benchmarks, using the same training data and similar amount of model parameters.
On the Language Coverage Bias for Neural Machine Translation
Language coverage bias, which indicates the content-dependent differences between sentence pairs originating from the source and target languages, is important for neural machine translation (NMT) because the target-original training data is not well exploited in current practice.
An Efficient Training Approach for Very Large Scale Face Recognition
After that, to reduce the training time and hardware costs of the FC layer, we propose a Dynamic Class Pool (DCP) that stores the features from Gallery Net and calculates the inner product (logits) with positive samples (whose identities are in the DCP) in each mini-batch.
Robust Training Using Natural Transformation
We target attributes of the input images that are independent of the class identification, and manipulate those attributes to mimic real-world natural transformations (NaTra) of the inputs, which are then used to augment the training dataset of the image classifier.
OCTOPUS: Overcoming Performance andPrivatization Bottlenecks in Distributed Learning
We introduce a new distributed learning scheme to address communication overhead via latent compression, leveraging global data while providing local privatization of local data without additional cost due to encryption or perturbation.
High-quality Low-dose CT Reconstruction Using Convolutional Neural Networks with Spatial and Channel Squeeze and Excitation
Low-dose computed tomography (CT) allows the reduction of radiation risk in clinical applications at the expense of image quality, which deteriorates the diagnosis accuracy of radiologists.
Product semantics translation from brain activity via adversarial learning
To modify a design semantic of a given product from personalised brain activity via adversarial learning, in this work, we propose a deep generative transformation model to modify product semantics from the brain signal.
IUP: An Intelligent Utility Prediction Scheme for Solid-State Fermentation in 5G IoT
In addition, we design a utility prediction model for SSF based on the Generative Adversarial Networks (GAN) and Fully Connected Neural Network (FCNN).
Multi-Pretext Attention Network for Few-shot Learning with Self-supervision
Self-supervised learning is emerged as an efficient method to utilize unlabeled data.
A General Framework for Revealing Human Mind with auto-encoding GANs
For this reason, we propose an end-to-end brain decoding framework which translates brain activity into an image by latent space alignment.
Beyond Fine-tuning: Classifying High Resolution Mammograms using Function-Preserving Transformations
The current state-of-the-art approaches for medical image classification rely on using the de-facto method for ConvNets - fine-tuning.
GenAD: General Representations of Multivariate Time Series for Anomaly Detection
However, due to complex correlations and various temporal patterns of large-scale multivariate time series, a general unsupervised anomaly detection model with higher F1-score and Timeliness remains a challenging task.
Learning Motion-Appearance Co-Attention for Zero-Shot Video Object Segmentation
How to make the appearance and motion information interact effectively to accommodate complex scenarios is a fundamental issue in flow-based zero-shot video object segmentation.
Neural Machine Translation: A Review of Methods, Resources, and Tools
Machine translation (MT) is an important sub-field of natural language processing that aims to translate natural languages using computers.
LCCNet: LiDAR and Camera Self-Calibration using Cost Volume Network
In this paper, we propose a novel online self-calibration approach for Light Detection and Ranging (LiDAR) and camera sensors.
ACDER: Augmented Curiosity-Driven Experience Replay
Exploration in environments with sparse feedback remains a challenging research problem in reinforcement learning (RL).
Semantic Flow-guided Motion Removal Method for Robust Mapping
In this paper, we proposed a novel motion removal method, leveraging semantic information and optical flow to extract motion regions.
Detection of Genuine and Posed Facial Expressions of Emotion: A Review
Facial expressions of emotion play an important role in human social interactions.
A Hybrid BERT and LightGBM based Model for Predicting Emotion GIF Categories on Twitter
The animated Graphical Interchange Format (GIF) images have been widely used on social media as an intuitive way of expression emotion.
A Multimodal Late Fusion Model for E-Commerce Product Classification
The cataloging of product listings is a fundamental problem for most e-commerce platforms.
Loss Function Search for Face Recognition
In face recognition, designing margin-based (e. g., angular, additive, additive angular margins) softmax loss functions plays an important role in learning discriminative features.
Suggestive Annotation of Brain Tumour Images with Gradient-guided Sampling
Machine learning has been widely adopted for medical image analysis in recent years given its promising performance in image segmentation and classification tasks.
Realistic Adversarial Data Augmentation for MR Image Segmentation
In this work, we propose an adversarial data augmentation method for training neural networks for medical image segmentation.
Deep Generative Model-based Quality Control for Cardiac MRI Segmentation
Our approach provides a real-time and model-agnostic quality control for cardiac MRI segmentation, which has the potential to be integrated into clinical image analysis workflows.
Adversarial Defense by Latent Style Transformations
The intuition behind our approach is that the essential characteristics of a normal image are generally consistent with non-essential style transformations, e. g., slightly changing the facial expression of human portraits.
Biomechanics-informed Neural Networks for Myocardial Motion Tracking in MRI
The learnt VAE regulariser then can be coupled with any deep learning based registration network to regularise the solution space to be biomechanically plausible.
On the Inference Calibration of Neural Machine Translation
Confidence calibration, which aims to make model predictions equal to the true correctness measures, is important for neural machine translation (NMT) because it is able to offer useful indicators of translation errors in the generated output.
PAMTRI: Pose-Aware Multi-Task Learning for Vehicle Re-Identification Using Highly Randomized Synthetic Data
In comparison with person re-identification (ReID), which has been widely studied in the research community, vehicle ReID has received less attention.
An Epidemiological Modelling Approach for Covid19 via Data Assimilation
We compare and discuss model results which conducts updates as new observations become available.
Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning
Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.
Efficient Deep Representation Learning by Adaptive Latent Space Sampling
Supervised deep learning requires a large amount of training samples with annotations (e. g. label class for classification task, pixel- or voxel-wised label map for segmentation tasks), which are expensive and time-consuming to obtain.
PM2.5-GNN: A Domain Knowledge Enhanced Graph Neural Network For PM2.5 Forecasting
When predicting PM2. 5 concentrations, it is necessary to consider complex information sources since the concentrations are influenced by various factors within a long period.
Understanding the Automated Parameter Optimization on Transfer Learning for CPDP: An Empirical Study
Given a tight computational budget, it is more cost-effective to focus on optimizing the parameter configuration of transfer learning algorithms (3) The research on CPDP is far from mature where it is "not difficult" to find a better alternative by making a combination of existing transfer learning and classification techniques.
Defending Adversarial Attacks via Semantic Feature Manipulation
In this paper, we propose a one-off and attack-agnostic Feature Manipulation (FM)-Defense to detect and purify adversarial examples in an interpretable and efficient manner.
Inference for Network Structure and Dynamics from Time Series Data via Graph Neural Network
We carried out experiments on discrete and continuous time series data.
OIAD: One-for-all Image Anomaly Detection with Disentanglement Learning
Our key insight is that the impact of small perturbation on the latent representation can be bounded for normal samples while anomaly images are usually outside such bounded intervals, referred to as structure consistency.
Backdoor Attacks against Transfer Learning with Pre-trained Deep Learning Models
In this paper, we demonstrate a backdoor threat to transfer learning tasks on both image and time-series data leveraging the knowledge of publicly accessible Teacher models, aimed at defeating three commonly-adopted defenses: \textit{pruning-based}, \textit{retraining-based} and \textit{input pre-processing-based defenses}.
Generating Semantic Adversarial Examples via Feature Manipulation
We further demonstrate the existence of a universal, image-agnostic semantic adversarial example.
A Two-stream End-to-End Deep Learning Network for Recognizing Atypical Visual Attention in Autism Spectrum Disorder
In this study, we developed a novel two-stream deep learning network for this recognition based on 700 images and corresponding eye movement patterns of ASD and TD, and obtained an accuracy of 0. 95, which was higher than the previous state-of-the-art.
Mis-classified Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progress due to the advances of deep convolutional neural networks (CNNs), the central task of which is how to improve the feature discrimination.
Automatic Brain Tumour Segmentation and Biophysics-Guided Survival Prediction
Gliomas are the most common malignant brain tumourswith intrinsic heterogeneity.
Time-Aware Gated Recurrent Unit Networks for Road Surface Friction Prediction Using Historical Data
The findings can help improve the prediction accuracy and efficiency of forecasting road surface friction using historical data sets with missing values, therefore mitigating the impact of wet or icy road conditions on traffic safety.
Road Surface Friction Prediction Using Long Short-Term Memory Neural Network Based on Historical Data
A precise road surface friction prediction model can help to alleviate the influence of inclement road conditions on traffic safety, Level of Service, traffic mobility, fuel efficiency, and sustained economic productivity.
REQ-YOLO: A Resource-Aware, Efficient Quantization Framework for Object Detection on FPGAs
To achieve real-time, highly-efficient implementations on FPGA, we present the detailed hardware implementation of block circulant matrices on CONV layers and develop an efficient processing element (PE) structure supporting the heterogeneous weight quantization, CONV dataflow and pipelining techniques, design optimization, and a template-based automatic synthesis framework to optimally exploit hardware resource.
Improving Back-Translation with Uncertainty-based Confidence Estimation
While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy.
Towards Defeating Misclassification Attacks Against Transfer Learning
To avoid the bloated ensemble size during inference, we propose two-phase defence, in which inference from the Student model is firstly performed to narrow down the candidate differentiators to be assembled, and later only a small, fixed number of them can be chosen to validate clean or reject adversarial inputs effectively.
Tutorial: Complexity analysis of Singular Value Decomposition and its variants
We compared the regular Singular Value Decomposition (SVD), truncated SVD, Krylov method and Randomized PCA, in terms of time and space complexity.
A support vector regression-based multi-fidelity surrogate model
Computational simulations with different fidelity have been widely used in engineering design.
CityFlow: A City-Scale Benchmark for Multi-Target Multi-Camera Vehicle Tracking and Re-Identification
Urban traffic optimization using traffic cameras as sensors is driving the need to advance state-of-the-art multi-target multi-camera (MTMC) tracking.
Hindsight Generative Adversarial Imitation Learning
Combining hindsight idea with the generative adversarial imitation learning (GAIL) framework, we realize implementing imitation learning successfully in cases of expert demonstration data are not available.
Improved Selective Refinement Network for Face Detection
With the availability of face detection benchmark WIDER FACE dataset, much of the progresses have been made by various algorithms in recent years.
A General Deep Learning Framework for Network Reconstruction and Dynamics Learning
We exhibit the universality of our framework on different kinds of time-series data: with the same structure, our model can be trained to accurately recover the network structure and predict future states on continuous, discrete, and binary dynamics, and outperforms competing network reconstruction methods.
Support Vector Guided Softmax Loss for Face Recognition
Face recognition has witnessed significant progresses due to the advances of deep convolutional neural networks (CNNs), the central challenge of which, is feature discrimination.
Ranked #1 on
Face Identification
on Trillion Pairs Dataset
Detect Globally, Refine Locally: A Novel Approach to Saliency Detection
Moreover, to effectively recover object boundaries, we propose a local Boundary Refinement Network (BRN) to adaptively learn the local contextual information for each spatial position.
Ranked #7 on
RGB Salient Object Detection
on DUTS-TE
Efficient Recurrent Neural Networks using Structured Matrices in FPGAs
Recurrent Neural Networks (RNNs) are becoming increasingly important for time series-related applications which require efficient and real-time implementations.
C-LSTM: Enabling Efficient LSTM using Structured Compression Techniques on FPGAs
The previous work proposes to use a pruning based compression technique to reduce the model size and thus speedups the inference on FPGAs.
Proceedings of the IJCAI 2017 Workshop on Learning in the Presence of Class Imbalance and Concept Drift (LPCICD'17)
It provides a forum for international researchers and practitioners to share and discuss their original work on addressing new challenges and research issues in class imbalance learning, concept drift, and the combined issues of class imbalance and concept drift.
A Systematic Study of Online Class Imbalance Learning with Concept Drift
As an emerging research topic, online class imbalance learning often combines the challenges of both class imbalance and concept drift.
Weakly Supervised Learning for Attribute Localization in Outdoor Scenes
We evaluate the proposed method by (i) showing the improvement of attribute recognition accuracy; and (ii) comparing the average precision of localizing attributes to the scene parts.