Anonymization of labeled TOF MRA images for brain vessel segmentation using generative adversarial networks

Anonymization and data sharing are crucial for privacy protection and acquisition of large datasets for medical image analysis . Here, the brain’s unique structure allows for re-identification and thus requires non-conventional anonymization . Generative adversarial networks (GANs) have the potential to provide anonymous images while preserving predictive properties .… Read the rest

Diversified Mutual Learning for Deep Metric Learning

Mutual learning is an ensemble training strategy to improve generalization by transferring individual knowledge to each other while simultaneously training multiple models . The proposed method with a conventional triplet loss achieves the state-of-the-art performance of [email protected] on standard datasets: 69.9 on CUB-200-2011 and 89.1 on CARS-196 .… Read the rest

Learned 3D Shape Representations Using Fused Geometrically Augmented Images Application to Facial Expression and Action Unit Detection

This paper proposes an approach to learn generic multi-modal mesh surface representations using a novel scheme for fusing texture and geometric data . Our approach defines an inverse mapping between different geometric descriptors computed on the mesh surface or its down-sampled version, and the corresponding 2D texture image of the mesh .… Read the rest

QiaoNing at SemEval 2020 Task 4 Commonsense Validation and Explanation system based on ensemble of language model

Language model system submitted to SemEval-2020 Task 4 competition: “Commonsense Validation and Explanation” We implemented with transfer learning using pretrained language models (BERT, XLNet, RoBERTa, and ALBERT) and fine-tune them on this task . The ensembled model better solves this problem, making the model’s accuracy reached 95.9% on subtask A, which just worse than human’s by only 3% accuracy .

MetaSleepLearner A Pilot Study on Fast Adaptation of Bio signals Based Sleep Stage Classifier to New Individual Subject Using Meta Learning

Identifying bio-signals based-sleep stages requires time-consuming and tedious labor of skilled clinicians . We propose the transfer learning framework, entitled MetaSleepLearner, based on Model Agnostic Meta-Learning (MAML) in order to transfer the acquired sleep staging knowledge from a large dataset to new individual subjects .… Read the rest

PIRC Net Using Proposal Indexing Relationships and Context for Phrase Grounding

Phrase Grounding aims to detect and localize objects in images that are referred to and are queried by natural language phrases . Phrase grounding finds applications in tasks such as Visual Dialog, Visual Search and Image-text co-reference resolution… In this paper, we present a framework that leverages information such as phrase category, relationships among neighboring phrases in a sentence and context to improve the performance of phrase grounding systems .… Read the rest

COVID MobileXpert On Device COVID 19 Patient Triage and Follow up using Chest X rays

COVID-MobileXpert: a lightweight deep neural network (DNN) based mobile app that can use chest X-ray (CXR) for case screening and radiological trajectory prediction… We design and implement a novel three-player knowledge transfer and distillation (KTD) framework including a pre-trained attending physician (AP) network that extracts CXR imaging features from a large scale of lung disease .… Read the rest

Driving Tasks Transfer in Deep Reinforcement Learning for Decision making of Autonomous Vehicles

Knowledge transfer is a promising concept to achieve real-time decision-making for autonomous vehicles . This paper constructs a transfer deep reinforcement learning framework to transform the driving tasks in inter-section environments… The driving missions at the un-signalized intersection are cast into a left turn, right turn, and running straight for automated vehicles .… Read the rest

kk2018 at SemEval 2020 Task 9 Adversarial Training for Code Mixing Sentiment Classification

Code switching is a linguistic phenomenon that may occur within a multilingual setting where speakers share more than one language . With the increasing communication between groups with different languages, this phenomenon is more and more popular… However, there are little research and data in this area, especially in code-mixing sentiment classification .… Read the rest

Random Style Transfer based Domain Generalization Networks Integrating Shape and Spatial Information

Deep learning (DL)-based models have demonstrated good performance in medical image segmentation . However, the models trained on a known dataset often fail when performed on an unseen dataset collected from different centers, vendors and disease populations . In this work, we present a random style transfer network to tackle the domain generalization problem .… Read the rest

Probabilistic Reasoning via Deep Learning Neural Association Models

In this paper, we propose a new deep learning approach, called neural association model (NAM), for probabilistic reasoning in artificial intelligence . We propose to use neural networks to model association between any two events in a domain . We take one event as input and compute a conditional probability of the other event to model how likely these two events are to be associated .… Read the rest

Light weight Head Pose Invariant Gaze Tracking

Unconstrained remote gaze tracking using off-the-shelf cameras is a challenging problem . We propose a novel branched CNN architecture that improves the robustness of gaze classifiers to variable head pose, without increasing computational cost . We also present various procedures to effectively train our gaze network including transfer learning from the more closely related task of object viewpoint estimation and from a large high-fidelity synthetic gaze dataset, which enable our ten times faster gaze network to achieve competitive accuracy to its current state of theart direct competitor .… Read the rest

Learning Shape Features and Abstractions in 3D Convolutional Neural Networks for Detecting Alzheimer s Disease

Deep Neural Networks – especially Convolutional Neural Network (ConvNet) has become the state-of-the-art for image classification, pattern recognition and various computer vision tasks . Early diagnosis is very crucial for preventing progress and treating the Alzheimer’s disease . Despite having the ability to deliver great performance, absence of interpretability of the model’s decision can lead to misdiagnosis which can be life threatening .… Read the rest

Privacy Analysis of Deep Learning in the Wild Membership Inference Attacks against Transfer Learning

Machine learning (ML) models are vulnerable to various security and privacy attacks . One major privacy attack in this domain is membership inference, where an adversary aims to determine whether a target data sample is part of the training set of a target ML model… So far, most of the current membership inference attacks are evaluated against ML models trained from scratch .… Read the rest

DGPose Deep Generative Models for Human Body Analysis

Deep generative modelling for human body analysis is an emerging problem with many interesting applications . But the latent space learned by such approaches is typically not interpretable, resulting in less flexibility… In this work, we present deep generative models in which the body pose and the visual appearance are disentangled .… Read the rest

Data Driven Transferred Energy Management Strategy for Hybrid Electric Vehicles via Deep Reinforcement Learning

Real-time applications of energy management strategies (EMSs) in hybrid electric vehicles are the harshest requirements for researchers and engineers . Inspired by the excellent problem-solving capabilities of deep reinforcement learning (DRL), this paper proposes a real-time EMS via incorporating the DRL method and transfer learning (TL) The EMSs related to the target driving cycles are estimated and compared in different training conditions .… Read the rest

Fashion Meets Computer Vision A Survey

This paper provides a comprehensive survey of more than 200 major fashion-related works . Fashion detection includes landmark detection, fashion parsing, and item retrieval . Fashion analysis contains attribute recognition, style learning, and popularity prediction . Fashion synthesis involves style transfer, pose transformation, and physical simulation .… Read the rest

Learning to Localize Actions from Moments

Action Herald Networks (AherNet) integrate such design into an one-stage action localization framework . The context of each moment is learnt through the adversarial mechanism to differentiate the generated features from those of background in untrimmed videos . Extensive experiments are conducted on the learning across the learning both across the splits of ActivityNet v1.3 and from THUMOS14 to ActivityNet .… Read the rest

On Transfer Learning of Traditional Frequency and Time Domain Features in Turning

There has been an increasing interest in leveraging machine learning tools for chatter prediction and diagnosis in discrete manufacturing processes . Traditional features for studying chatter include traditional signal processing tools such as Fast Fourier Transform (FFT), Power Spectral Density (PSD), and the Auto-correlation Function (ACF)… In this study, we use these tools in a supervised learning setting to identify chatter in accelerometer signals obtained from a turning experiment .… Read the rest

Evaluating Knowledge Transfer In Neural Network for Medical Images

Deep learning and knowledge transfer techniques have permeated the field of medical imaging and are considered as key approaches for revolutionizing diagnostic imaging practices . However, there are still challenges for the successful integration of deep learning into medical imaging tasks due to a lack of large annotated imaging data… To address this issue, we propose a teacher-student learning framework to transfer knowledge from a carefully pre-trained convolutional neural network (CNN) teacher to a student CNN .… Read the rest

Overcoming Negative Transfer A Survey

Transfer learning aims to help the target task with little or no training data by leveraging knowledge from one or multi-related auxiliary tasks . Negative transfer is a long-standing problem in transfer learning literature, which has been well recognized within the transfer learning community… How to overcome negative transfer has been studied for a long time and has raised increasing attention in recent years .… Read the rest

Groove2Groove One Shot Music Style Transfer with Supervision from Synthetic Data

Style transfer is the process of changing the style of an image, video, audio clip or musical piece so as to match a given example . The task has interesting practical applications within the music industry, but it has so far received little attention from the audio and music processing community… In this paper, we present Groove2Groove, a one-shot style transfer method for symbolic music, focusing on the case of accompaniment styles in popular music and jazz .… Read the rest

A Comprehensive Analysis of Information Leakage in Deep Transfer Learning

Transfer learning is widely used for transferring knowledge from a source domain to the target domain where the labeled data is scarce . However, the source and target datasets usually belong to two different organizations in many real-world scenarios . In this study, to thoroughly analyze the potential privacy leakage in deep transfer learning, we first divide previous methods into three categories .… Read the rest

Large Dimensional Analysis and Improvement of Multi Task Learning

Multi Task Learning (MTL) efficiently leverages useful information contained in multiple related tasks to help improve the generalization performance of all tasks . This article conducts a large dimensional analysis of a simple but, as we shall see, extremely powerful when carefully tuned, Least Square Support Vector Machine (LSSVM) version of MTL, in the regime where the dimension $p$ of the data and their number $n$ grow large at the same rate .… Read the rest

A Lightweight Music Texture Transfer System

Deep learning researches on the transformation problems for image and text have raised great attention . However, present methods for music feature transfer using neural networks are far from practical application… In this paper, we initiate a novel system for transferring the texture of music, and release it as an open source project .… Read the rest