Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
Deep semi-supervised segmentation with weight-averaged consistency targets
Recently proposed techniques for semi-supervised learning such as Temporal Ensembling and Mean Teacher have achieved state-of-the-art results in many important classification benchmarks. In this work, we expand the Mean …
Data AugmentationGeneral ClassificationSegmentationSmall Data Image ClassificationBridging CNNs, RNNs, and Weighted Finite-State Machines
Recurrent and convolutional neural networks comprise two distinct families of models that have proven to be useful for encoding natural language utterances. In this paper we present SoPa, a new model that aims to bridge …
General ClassificationRepresentation LearningSmall Data Image Classificationtext-classification+1A Named Entity Recognition Shootout for German
We ask how to practically build a model for German named entity recognition (NER) that performs at the state of the art for both contemporary and historical texts, i.e., a big-data and a small-data scenario. The two best…
Entity Linkingnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+5Learning to Promote Saliency Detectors
The categories and appearance of salient objects vary from image to image, therefore, saliency detection is an image-specific task. Due to lack of large-scale saliency training data, using deep neural networks (DNNs) wit…
Saliency DetectionSmall Data Image ClassificationZero-Shot LearningOLÃ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep Learning
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin o…
General Classificationimage-classificationImage ClassificationMetric Learning+3Network Signatures from Image Representation of Adjacency Matrices: Deep/Transfer Learning for Subgraph Classification
We propose a novel subgraph image representation for classification of network fragments with the targets being their parent networks. The graph image representation is based on 2D image embeddings of adjacency matrices.…
General ClassificationSmall Data Image ClassificationTransfer LearningLarge-Scale Cox Process Inference using Variational Fourier Features
Gaussian process modulated Poisson processes provide a flexible framework for modelling spatiotemporal point patterns. So far this had been restricted to one dimension, binning to a pre-determined grid, or small data set…
GPUSmall Data Image ClassificationMeta Reinforcement Learning with Latent Variable Gaussian Processes
Learning from small data sets is critical in many practical applications where data collection is time consuming or expensive, e.g., robotics, animal experiments or drug design. Meta learning is one way to increase the d…
Drug DesignGaussian ProcessesMeta-LearningMeta Reinforcement Learning+5Capturing Structure Implicitly from Time-Series having Limited Data
Scientific fields such as insider-threat detection and highway-safety planning often lack sufficient amounts of time-series data to estimate statistical models for the purpose of scientific discovery. Moreover, the avail…
scientific discoverySmall Data Image ClassificationTime SeriesTime Series AnalysisPredicting Oral Disintegrating Tablet Formulations by Neural Network Techniques
Oral Disintegrating Tablets (ODTs) is a novel dosage form that can be dissolved on the tongue within 3min or less especially for geriatric and pediatric patients. Current ODT formulation studies usually rely on the perso…
PredictionSmall Data Image ClassificationLocal Contrast Learning
Learning a deep model from small data is yet an opening and challenging problem. We focus on one-shot classification by deep learning approach based on a small quantity of training samples. We proposed a novel deep learn…
General ClassificationSmall Data Image ClassificationLie Transform--based Neural Networks for Dynamics Simulation and Learning
In the article, we discuss the architecture of the polynomial neural network that corresponds to the matrix representation of Lie transform. The matrix form of Lie transform is an approximation of the general solution of…
Small Data Image ClassificationTime Series AnalysisVIBNN: Hardware Acceleration of Bayesian Neural Networks
Bayesian Neural Networks (BNNs) have been proposed to address the problem of model uncertainty in training and inference. By introducing weights associated with conditioned probability distributions, BNNs are capable of …
Small Data Image ClassificationVariational InferenceDeep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem
We address the problem to tackle the very similar objects like Chihuahua or muffin problem to recognize at least in human vision level. Our regular deep structured machine learning still does not solve it. We saw many ti…
BIG-bench Machine LearningSmall Data Image ClassificationTransfer LearningRepresentation Learning with Autoencoders for Electronic Health Records: A Comparative Study
Increasing volume of Electronic Health Records (EHR) in recent years provides great opportunities for data scientists to collaborate on different aspects of healthcare research by applying advanced analytics to these EHR…
Representation LearningSmall Data Image ClassificationOLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep Learning
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification. Yet, this does not naturally enforce intra-class similarity nor inter-class margin o…
General Classificationimage-classificationImage ClassificationMetric Learning+3One-Shot Coresets: The Case of k-Clustering
Scaling clustering algorithms to massive data sets is a challenging task. Recently, several successful approaches based on data summarization methods, such as coresets and sketches, were proposed. While these techniques …
ClusteringData SummarizationSmall Data Image ClassificationLDMNet: Low Dimensional Manifold Regularized Neural Networks
Deep neural networks have proved very successful on archetypal tasks for which large training sets are available, but when the training data are scarce, their performance suffers from overfitting. Many existing methods o…
Face RecognitionSmall Data Image ClassificationCapturing Localized Image Artifacts through a CNN-based Hyper-image Representation
Training deep CNNs to capture localized image artifacts on a relatively small dataset is a challenging task. With enough images at hand, one can hope that a deep CNN characterizes localized artifacts over the entire data…
Image Quality EstimationSmall Data Image ClassificationQuality-Efficiency Trade-offs in Machine Learning for Text Processing
Data mining, machine learning, and natural language processing are powerful techniques that can be used together to extract information from large texts. Depending on the task or problem at hand, there are many different…
BIG-bench Machine LearningDocument Classificationnamed-entity-recognitionNamed Entity Recognition+3