Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
Algorithmic Probability-guided Supervised Machine Learning on Non-differentiable Spaces
We show how complexity theory can be introduced in machine learning to help bring together apparently disparate areas of current research. We show that this new approach requires less training data and is more generaliza…
BIG-bench Machine LearningGeneral Classificationimage-classificationImage Classification+2Deep Kernel Learning via Random Fourier Features
Kernel learning methods are among the most effective learning methods and have been vigorously studied in the past decades. However, when tackling with complicated tasks, classical kernel methods are not flexible or "ric…
image-classificationImage ClassificationSmall Data Image ClassificationFully Convolutional Networks for Chip-wise Defect Detection Employing Photoluminescence Images
Efficient quality control is inevitable in the manufacturing of light-emitting diodes (LEDs). Because defective LED chips may be traced back to different causes, a time and cost-intensive electrical and optical contact m…
Defect DetectionSmall Data Image ClassificationHarnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks
Recent research shows that the following two models are equivalent: (a) infinitely wide neural networks (NNs) trained under l2 loss by gradient descent with infinitesimally small learning rate (b) kernel regression with …
Few-Shot Image ClassificationGeneral Classificationimage-classificationImage Classification+3Deep learning for Chemometric and non-translational data
We propose a novel method to train deep convolutional neural networks which learn from multiple data sets of varying input sizes through weight sharing. This is an advantage in chemometrics where individual measurements …
Deep LearningSmall Data Image ClassificationTransfer LearningLatent-Variable Generative Models for Data-Efficient Text Classification
Generative classifiers offer potential advantages over their discriminative counterparts, namely in the areas of data efficiency, robustness to data shift and adversarial examples, and zero-shot learning (Ng and Jordan,2…
ClassificationGeneral ClassificationLanguage ModelingLanguage Modelling+4PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs
Physics-informed neural networks (PINNs) encode physical conservation laws and prior physical knowledge into the neural networks, ensuring the correct physics is represented accurately while alleviating the need for supe…
Small Data Image ClassificationAutomated detection of oral pre-cancerous tongue lesions using deep learning for early diagnosis of oral cavity cancer
Discovering oral cavity cancer (OCC) at an early stage is an effective way to increase patient survival rate. However, current initial screening process is done manually and is expensive for the average individual, espec…
General ClassificationManagementSmall Data Image ClassificationSpecificity+1Global Autoregressive Models for Data-Efficient Sequence Learning
Standard autoregressive seq2seq models are easily trained by max-likelihood, but tend to show poor results under small-data conditions. We introduce a class of seq2seq models, GAMs (Global Autoregressive Models), which c…
Language ModellingSmall Data Image ClassificationEvaluating the Consistency of Word Embeddings from Small Data
In this work, we address the evaluation of distributional semantic models trained on smaller, domain-specific texts, specifically, philosophical text. Specifically, we inspect the behaviour of models using a pre-trained …
Small Data Image ClassificationWord EmbeddingsRepresentation 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 ClassificationEffect of Activation Functions on the Training of Overparametrized Neural Nets
It is well-known that overparametrized neural networks trained using gradient-based methods quickly achieve small training error with appropriate hyperparameter settings. Recent papers have proved this statement theoreti…
Small Data Image ClassificationRobust data-driven approach for predicting the configurational energy of high entropy alloys
High entropy alloys (HEAs) have been increasingly attractive as promising next-generation materials due to their various excellent properties. It's necessary to essentially characterize the degree of chemical ordering an…
feature selectionSmall Data Image ClassificationLess (Data) Is More: Why Small Data Holds the Key to the Future of Artificial Intelligence
The claims that big data holds the key to enterprise successes and that Artificial Intelligence is going to replace humanity have become increasingly more popular over the past few years, both in academia and in the indu…
Small Data Image ClassificationShrinkML: End-to-End ASR Model Compression Using Reinforcement Learning
End-to-end automatic speech recognition (ASR) models are increasingly large and complex to achieve the best possible accuracy. In this paper, we build an AutoML system that uses reinforcement learning (RL) to optimize th…
Automatic Speech RecognitionAutomatic Speech Recognition (ASR)AutoMLModel Compression+6Competing Models
Different agents need to make a prediction. They observe identical data, but have different models: they predict using different explanatory variables. We study which agent believes they have the best predictive ability …
Model SelectionSmall Data Image ClassificationSelf-Supervised Learning for Cardiac MR Image Segmentation by Anatomical Position Prediction
In the recent years, convolutional neural networks have transformed the field of medical image analysis due to their capacity to learn discriminative image features for a variety of classification and regression tasks. H…
Image SegmentationMedical Image AnalysisPositionSegmentation+3Bayesian inverse regression for dimension reduction with small datasets
We consider supervised dimension reduction problems, namely to identify a low dimensional projection of the predictors $\-x$ which can retain the statistical relationship between $\-x$ and the response variable $y$. We f…
Dimensionality ReductionregressionSmall Data Image ClassificationA database for face presentation attack using wax figure faces
Compared to 2D face presentation attacks (e.g. printed photos and video replays), 3D type attacks are more challenging to face recognition systems (FRS) by presenting 3D characteristics or materials similar to real faces…
DiversityFace Presentation Attack DetectionFace RecognitionSmall Data Image ClassificationData-efficient Neural Text Compression with Interactive Learning
Neural sequence-to-sequence models have been successfully applied to text compression. However, these models were trained on huge automatically induced parallel corpora, which are only available for a few domains and tas…
Active LearningHeadline GenerationSmall Data Image ClassificationText Compression