Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease
Diffusion MRI is the modality of choice to study alterations of white matter. In past years, various works have used diffusion MRI for automatic classification of AD. However, classification performance obtained with dif…
ClassificationDiffusion MRIGeneral ClassificationSmall Data Image ClassificationFully Automatic Segmentation of Sublingual Veins from Retrained U-Net Model for Few Near Infrared Images
Sublingual vein is commonly used to diagnose the health status. The width of main sublingual veins gives information of the blood circulation. Therefore, it is necessary to segment the main sublingual veins from the tong…
Data AugmentationSegmentationSmall Data Image ClassificationSynergy Effect between Convolutional Neural Networks and the Multiplicity of SMILES for Improvement of Molecular Prediction
In our study, we demonstrate the synergy effect between convolutional neural networks and the multiplicity of SMILES. The model we propose, the so-called Convolutional Neural Fingerprint (CNF) model, reaches the accuracy…
Ensemble LearningSmall Data Image ClassificationModern Neural Networks Generalize on Small Data Sets
In this paper, we use a linear program to empirically decompose fitted neural networks into ensembles of low-bias sub-networks. We show that these sub-networks are relatively uncorrelated which leads to an internal re…
General Classificationimage-classificationImage ClassificationSmall Data Image ClassificationCompact Representation of Uncertainty in Clustering
For many classic structured prediction problems, probability distributions over the dependent variables can be efficiently computed using widely-known algorithms and data structures (such as forward-backward, and its cor…
ClusteringSmall Data Image ClassificationStructured PredictionCombining Deep Learning and Argumentative Reasoning for the Analysis of Social Media Textual Content Using Small Data Sets
The use of social media has become a regular habit for many and has changed the way people interact with each other. In this article, we focus on analyzing whether news headlines support tweets and whether reviews are de…
Argument MiningArticlesDeception DetectionFact Checking+1A Deep Latent-Variable Model Application to Select Treatment Intensity in Survival Analysis
In the following short article we adapt a new and popular machine learning model for inference on medical data sets. Our method is based on the Variational AutoEncoder (VAE) framework that we adapt to survival analysis o…
BIG-bench Machine LearningDecision MakingMissing ValuesSmall Data Image Classification+1Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection
The task of localizing and categorizing objects in medical images often remains formulated as a semantic segmentation problem. This approach, however, only indirectly solves the coarse localization task by predicting pix…
Medical Image AnalysisMedical Object DetectionObjectobject-detection+4Learning with tree-based tensor formats
This paper is concerned with the approximation of high-dimensional functions in a statistical learning setting, by empirical risk minimization over model classes of functions in tree-based tensor format. These are partic…
Small Data Image ClassificationAn Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon Drawing
In this paper, we explore creative image generation constrained by small data. To partially automate the creation of cartoon sketches consistent with a specific designer's style, where acquiring a very large original ima…
Image GenerationSmall Data Image ClassificationDragonPaint: Rule based bootstrapping for small data with an application to cartoon coloring
In this paper, we confront the problem of deep learning's big labeled data requirements, offer a rule based strategy for extreme augmentation of small data sets and apply that strategy with the image to image translation…
Image-to-Image TranslationSmall Data Image ClassificationTranslationLow-shot Learning via Covariance-Preserving Adversarial Augmentation Networks
Deep neural networks suffer from over-fitting and catastrophic forgetting when trained with small data. One natural remedy for this problem is data augmentation, which has been recently shown to be effective. However, pr…
Data AugmentationGenerative Adversarial NetworkSmall Data Image ClassificationAutomatic Brain Tumor Segmentation using Convolutional Neural Networks with Test-Time Augmentation
Automatic brain tumor segmentation plays an important role for diagnosis, surgical planning and treatment assessment of brain tumors. Deep convolutional neural networks (CNNs) have been widely used for this task. Due to …
Brain Tumor SegmentationData AugmentationSegmentationSmall Data Image Classification+1Annotation of a Large Clinical Entity Corpus
Having an entity annotated corpus of the clinical domain is one of the basic requirements for detection of clinical entities using machine learning (ML) approaches. Past researches have shown the superiority of statistic…
Machine TranslationSmall Data Image ClassificationLearning to Actively Learn Neural Machine Translation
Traditional active learning (AL) methods for machine translation (MT) rely on heuristics. However, these heuristics are limited when the characteristics of the MT problem change due to e.g. the language pair or the amoun…
Active LearningImitation LearningMachine TranslationSentence+2The Fast and the Flexible: training neural networks to learn to follow instructions from small data
Learning to follow human instructions is a long-pursued goal in artificial intelligence. The task becomes particularly challenging if no prior knowledge of the employed language is assumed while relying only on a handful…
Inductive BiasSmall Data Image ClassificationMultiple-gaze geometry: Inferring novel 3D locations from gazes observed in monocular video
We develop using person gaze direction for scene understanding. In particular, we use intersecting gazes to learn 3D locations that people tend to look at, which is analogous to having multiple camera views. The 3D locat…
Scene UnderstandingSmall Data Image ClassificationSmall Sample Learning in Big Data Era
As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. In this paper, we aim to present a survey…
Small Data Image ClassificationA Semi-Supervised Data Augmentation Approach using 3D Graphical Engines
Deep learning approaches have been rapidly adopted across a wide range of fields because of their accuracy and flexibility, but require large labeled training datasets. This presents a fundamental problem for application…
Data AugmentationDeep LearningDomain AdaptationPose Estimation+2Improving Transferability of Deep Neural Networks
Learning from small amounts of labeled data is a challenge in the area of deep learning. This is currently addressed by Transfer Learning where one learns the small data set as a transfer task from a larger source datase…
image-classificationImage ClassificationSmall Data Image ClassificationTransfer Learning