Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017
In this paper, we describe the NICT-2 neural machine translation system evaluated at WAT2017. This system uses multiple models as an ensemble and combines models with opposite decoding directions by reranking (called bi-…
Machine TranslationRerankingSmall Data Image ClassificationTranslationImproving Landmark Localization with Semi-Supervised Learning
We present two techniques to improve landmark localization in images from partially annotated datasets. Our primary goal is to leverage the common situation where precise landmark locations are only provided for a small …
Face AlignmentSmall Data Image Classification`Fighting' or `Conflict'? An Approach to Revealing Concepts of Terms in Political Discourse
Previous work on the epistemology of fact-checking indicated the dilemma between the needs of binary answers for the public and ambiguity of political discussion. Determining concepts represented by terms in political di…
ClusteringFact CheckingSmall Data Image ClassificationWord Sense DisambiguationHidden Physics Models: Machine Learning of Nonlinear Partial Differential Equations
While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire. In this paper, we present a new paradigm of learning partial differential equations from {\em small}…
BIG-bench Machine LearningGaussian ProcessesSmall Data Image ClassificationUnsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity Data
One of the main benefits of a wrist-worn computer is its ability to collect a variety of physiological data in a minimally intrusive manner. Among these data, electrodermal activity (EDA) is readily collected and provide…
Artifact DetectionSmall Data Image ClassificationRepresentation Learning on Large and Small Data
Deep learning owes its success to three key factors: scale of data, enhanced models to learn representations from data, and scale of computation. This book chapter presented the importance of the data-driven approach to …
Melanoma DiagnosisRepresentation LearningSmall Data Image ClassificationHigh-risk learning: acquiring new word vectors from tiny data
Distributional semantics models are known to struggle with small data. It is generally accepted that in order to learn 'a good vector' for a word, a model must have sufficient examples of its usage. This contradicts the …
Language ModelingLanguage ModellingSmall Data Image ClassificationVocal Bursts Intensity PredictionLearning like humans with Deep Symbolic Networks
We introduce the Deep Symbolic Network (DSN) model, which aims at becoming the white-box version of Deep Neural Networks (DNN). The DSN model provides a simple, universal yet powerful structure, similar to DNN, to repres…
Small Data Image ClassificationA simple efficient density estimator that enables fast systematic search
This paper introduces a simple and efficient density estimator that enables fast systematic search. To show its advantage over commonly used kernel density estimator, we apply it to outlying aspects mining. Outlying aspe…
Small Data Image ClassificationSynthesizing Deep Neural Network Architectures using Biological Synaptic Strength Distributions
In this work, we perform an exploratory study on synthesizing deep neural networks using biological synaptic strength distributions, and the potential influence of different distributions on modelling performance particu…
Small Data Image ClassificationInterpretable Structure-Evolving LSTM
This paper develops a general framework for learning interpretable data representation via Long Short-Term Memory (LSTM) recurrent neural networks over hierarchal graph structures. Instead of learning LSTM models over th…
Small Data Image ClassificationInteractive Elicitation of Knowledge on Feature Relevance Improves Predictions in Small Data Sets
Providing accurate predictions is challenging for machine learning algorithms when the number of features is larger than the number of samples in the data. Prior knowledge can improve machine learning models by indicatin…
BIG-bench Machine LearningPredictionSmall Data Image ClassificationImproving word alignment for low resource languages using English monolingual SRL
We introduce a new statistical machine translation approach specifically geared to learning translation from low resource languages, that exploits monolingual English semantic parsing to bias inversion transduction gramm…
Machine TranslationMemorizationSemantic ParsingSmall Data Image Classification+2QCRI @ DSL 2016: Spoken Arabic Dialect Identification Using Textual Features
The paper describes the QCRI submissions to the task of automatic Arabic dialect classification into 5 Arabic variants, namely Egyptian, Gulf, Levantine, North-African, and Modern Standard Arabic (MSA). The training data…
Dialect IdentificationMachine TranslationSmall Data Image ClassificationRelaxed Earth Mover's Distances for Chain- and Tree-connected Spaces and their use as a Loss Function in Deep Learning
The Earth Mover's Distance (EMD) computes the optimal cost of transforming one distribution into another, given a known transport metric between them. In deep learning, the EMD loss allows us to embed information during …
Deep LearningSmall Data Image ClassificationNazr-CNN: Fine-Grained Classification of UAV Imagery for Damage Assessment
We propose Nazr-CNN1, a deep learning pipeline for object detection and fine-grained classification in images acquired from Unmanned Aerial Vehicles (UAVs) for damage assessment and monitoring. Nazr-CNN consists of two c…
General Classificationobject-detectionObject DetectionSmall Data Image Classification+1Ordinal Constrained Binary Code Learning for Nearest Neighbor Search
Recent years have witnessed extensive attention in binary code learning, a.k.a. hashing, for nearest neighbor search problems. It has been seen that high-dimensional data points can be quantized into binary codes to give…
RetrievalSmall Data Image ClassificationBeyond Fine Tuning: A Modular Approach to Learning on Small Data
In this paper we present a technique to train neural network models on small amounts of data. Current methods for training neural networks on small amounts of rich data typically rely on strategies such as fine-tuning a …
Small Data Image ClassificationTransfer LearningCuMF_SGD: Fast and Scalable Matrix Factorization
Matrix factorization (MF) has been widely used in e.g., recommender systems, topic modeling and word embedding. Stochastic gradient descent (SGD) is popular in solving MF problems because it can deal with large data sets…
CPUGPUIncremental LearningRecommendation Systems+1FaceNet2ExpNet: Regularizing a Deep Face Recognition Net for Expression Recognition
Relatively small data sets available for expression recognition research make the training of deep networks for expression recognition very challenging. Although fine-tuning can partially alleviate the issue, the perform…
Face RecognitionFacial Expression Recognition (FER)Small Data Image Classification