Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
Bayesian Learning for Neural Dependency Parsing
While neural dependency parsers provide state-of-the-art accuracy for several languages, they still rely on large amounts of costly labeled training data. We demonstrate that in the small data regime, where uncertainty a…
Dependency Parsingparameter estimationPOSPOS Tagging+1Enhancing Simple Models by Exploiting What They Already Know
There has been recent interest in improving performance of simple models for multiple reasons such as interpretability, robust learning from small data, deployment in memory constrained settings as well as environmental …
Small Data Image ClassificationGuided Source Separation Meets a Strong ASR Backend: Hitachi/Paderborn University Joint Investigation for Dinner Party ASR
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario. The main challenges of ASR systems for dinner party recordings obtained by mult…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)Small Data Image ClassificationSpeech Enhancement+2Learning What and Where to Transfer
As the application of deep learning has expanded to real-world problems with insufficient volume of training data, transfer learning recently has gained much attention as means of improving the performance in such small-…
Meta-LearningSmall Data Image ClassificationTransfer LearningDeepMoD: Deep learning for Model Discovery in noisy data
We introduce DeepMoD, a Deep learning based Model Discovery algorithm. DeepMoD discovers the partial differential equation underlying a spatio-temporal data set using sparse regression on a library of possible functions …
Deep LearningModel DiscoveryregressionSmall Data Image Classification+2SCANN: Synthesis of Compact and Accurate Neural Networks
Deep neural networks (DNNs) have become the driving force behind recent artificial intelligence (AI) research. An important problem with implementing a neural network is the design of its architecture. Typically, such an…
Dimensionality ReductionNeural Network CompressionSmall Data Image ClassificationSSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor Data
Missing data are unavoidable in wireless sensor networks, due to issues such as network communication outage, sensor maintenance or failure, etc. Although a plethora of methods have been proposed for imputing sensor data…
ImputationSmall Data Image ClassificationSSIMTime Series+1Small Data Challenges in Big Data Era: A Survey of Recent Progress on Unsupervised and Semi-Supervised Methods
Representation learning with small labeled data have emerged in many problems, since the success of deep neural networks often relies on the availability of a huge amount of labeled data that is expensive to collect. To …
Domain AdaptationRepresentation LearningSmall Data Image ClassificationDo we still need fuzzy classifiers for Small Data in the Era of Big Data?
The Era of Big Data has forced researchers to explore new distributed solutions for building fuzzy classifiers, which often introduce approximation errors or make strong assumptions to reduce computational and memory req…
Small Data Image ClassificationNovel quantitative indicators of digital ophthalmoscopy image quality
With the advent of smartphone indirect ophthalmoscopy, teleophthalmology - the use of specialist ophthalmology assets at a distance from the patient - has experienced a breakthrough, promising enormous benefits especiall…
Image Quality AssessmentSmall Data Image ClassificationLung CT Imaging Sign Classification through Deep Learning on Small Data
The annotated medical images are usually expensive to be collected. This paper proposes a deep learning method on small data to classify Common Imaging Signs of Lung diseases (CISL) in computed tomography (CT) images. We…
Binary ClassificationComputed Tomography (CT)General ClassificationGenerative Adversarial Network+2A Distributionally Robust Optimization Method for Adversarial Multiple Kernel Learning
We propose a novel data-driven method to learn a mixture of multiple kernels with random features that is certifiabaly robust against adverserial inputs. Specifically, we consider a distributionally robust optimization o…
Generalization BoundsModel SelectionSemantic SegmentationSmall Data Image ClassificationHuman-in-the-loop Active Covariance Learning for Improving Prediction in Small Data Sets
Learning predictive models from small high-dimensional data sets is a key problem in high-dimensional statistics. Expert knowledge elicitation can help, and a strong line of work focuses on directly eliciting informative…
Decision MakingSequential Decision MakingSmall Data Image ClassificationMIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender Systems
The human mind is still an unknown process of neuroscience in many aspects. Nevertheless, for decades the scientific community has proposed computational models that try to simulate their parts, specific applications, or…
Movie RecommendationRecommendation SystemsSmall Data Image ClassificationPretrained language model transfer on neural named entity recognition in Indonesian conversational texts
Named entity recognition (NER) is an important task in NLP, which is all the more challenging in conversational domain with their noisy facets. Moreover, conversational texts are often available in limited amount, making…
Language ModelingLanguage Modellingnamed-entity-recognitionNamed Entity Recognition+4A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regime
The automated construction of coarse-grained models represents a pivotal component in computer simulation of physical systems and is a key enabler in various analysis and design tasks related to uncertainty quantificatio…
Small Data Image ClassificationUncertainty QuantificationVariational InferenceFast Hyperparameter Tuning using Bayesian Optimization with Directional Derivatives
In this paper we develop a Bayesian optimization based hyperparameter tuning framework inspired by statistical learning theory for classifiers. We utilize two key facts from PAC learning theory; the generalization bound …
Bayesian OptimizationLearning TheoryPAC learningSmall Data Image ClassificationActive Image Synthesis for Efficient Labeling
The great success achieved by deep neural networks attracts increasing attention from the manufacturing and healthcare communities. However, the limited availability of data and high costs of data collection are the majo…
Image GenerationSmall Data Image ClassificationPhysics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
Surrogate modeling and uncertainty quantification tasks for PDE systems are most often considered as supervised learning problems where input and output data pairs are used for training. The construction of such emulator…
Small Data Image ClassificationUncertainty QuantificationVisual Feature Fusion and its Application to Support Unsupervised Clustering Tasks
On visual analytics applications, the concept of putting the user on the loop refers to the ability to replace heuristics by user knowledge on machine learning and data mining tasks. On supervised tasks, the user engagem…
BIG-bench Machine LearningClusteringSmall Data Image Classification