Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
A Close Look at Deep Learning with Small Data
In this work, we perform a wide variety of experiments with different deep learning architectures on datasets of limited size. According to our study, we show that model complexity is a critical factor when only a few sa…
Data AugmentationDeep LearningSmall Data Image ClassificationMix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning
This paper studies the problem of post-hoc calibration of machine learning classifiers. We introduce the following desiderata for uncertainty calibration: (a) accuracy-preserving, (b) data-efficient, and (c) high express…
Small Data Image ClassificationOn Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial location by learning filters that respond e…
General Classificationimage-classificationImage ClassificationInductive Bias+4Deep Neural Review Text Interaction for Recommendation Systems
Users' reviews contain valuable information which are not taken into account in most recommender systems. According to the latest studies in this field, using review texts could not only improve the performance of recomm…
Recommendation SystemsSmall Data Image ClassificationText MatchingAdaptive Name Entity Recognition under Highly Unbalanced Data
For several purposes in Natural Language Processing (NLP), such as Information Extraction, Sentiment Analysis or Chatbot, Named Entity Recognition (NER) holds an important role as it helps to determine and categorize ent…
Chatbotnamed-entity-recognitionNamed Entity RecognitionNamed Entity Recognition (NER)+3Unbiased Mean Teacher for Cross-domain Object Detection
Cross-domain object detection is challenging, because object detection model is often vulnerable to data variance, especially to the considerable domain shift between two distinctive domains. In this paper, we propose a …
Objectobject-detectionObject DetectionSmall Data Image Classification+1Rigid-Soft Interactive Learning for Robust Grasping
Inspired by widely used soft fingers on grasping, we propose a method of rigid-soft interactive learning, aiming at reducing the time of data collection. In this paper, we classify the interaction categories into Rigid-R…
Small Data Image ClassificationData Structures & Algorithms for Exact Inference in Hierarchical Clustering
Hierarchical clustering is a fundamental task often used to discover meaningful structures in data, such as phylogenetic trees, taxonomies of concepts, subtypes of cancer, and cascades of particle decays in particle phys…
ClusteringSmall Data Image ClassificationPerformance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs
Extracting large amounts of data from biological samples is not feasible due to radiation issues, and image processing in the small-data regime is one of the critical challenges when working with a limited amount of data…
Small Data Image ClassificationBetter Classifier Calibration for Small Data Sets
Classifier calibration does not always go hand in hand with the classifier's ability to separate the classes. There are applications where good classifier calibration, i.e. the ability to produce accurate probability est…
Classifier calibrationSmall Data Image ClassificationMeta-learning for mixed linear regression
In modern supervised learning, there are a large number of tasks, but many of them are associated with only a small amount of labeled data. These include data from medical image processing and robotic interaction. Even t…
Meta-LearningregressionSmall Data Image ClassificationOn Coresets for Support Vector Machines
We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points…
Small Data Image ClassificationOn a scalable entropic breaching of the overfitting barrier in machine learning
Overfitting and treatment of "small data" are among the most challenging problems in the machine learning (ML), when a relatively small data statistics size $T$ is not enough to provide a robust ML fit for a relatively l…
BIG-bench Machine LearningClassificationGeneral ClassificationRobust classification+1Better Multi-class Probability Estimates for Small Data Sets
Many classification applications require accurate probability estimates in addition to good class separation but often classifiers are designed focusing only on the latter. Calibration is the process of improving probabi…
ClassificationGeneral ClassificationSmall Data Image ClassificationBig-Data Science in Porous Materials: Materials Genomics and Machine Learning
By combining metal nodes with organic linkers we can potentially synthesize millions of possible metal organic frameworks (MOFs). At present, we have libraries of over ten thousand synthesized materials and millions of i…
BIG-bench Machine LearningSmall Data Image ClassificationIdentifying Table Structure in Documents using Conditional Generative Adversarial Networks
In many industries, as well as in academic research, information is primarily transmitted in the form of unstructured documents (this article, for example). Hierarchically-related data is rendered as tables, and extracti…
Generative Adversarial NetworkSmall Data Image ClassificationIncorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systems
Data-based discovery of effective, coarse-grained (CG) models of high-dimensional dynamical systems presents a unique challenge in computational physics and particularly in the context of multiscale problems. The present…
BIG-bench Machine LearningSmall Data Image ClassificationRandomly Projected Additive Gaussian Processes for Regression
Gaussian processes (GPs) provide flexible distributions over functions, with inductive biases controlled by a kernel. However, in many applications Gaussian processes can struggle with even moderate input dimensionality.…
Gaussian ProcessesregressionSmall Data Image ClassificationMRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation
Magnetic resonance (MR) imaging is an essential diagnostic tool in clinical medicine. Recently, a variety of deep learning methods have been applied to segmentation tasks in medical images, with promising results for com…
DiagnosticSmall Data Image ClassificationTowards Hardware-Aware Tractable Learning of Probabilistic Models
Smart portable applications increasingly rely on edge computing due to privacy and latency concerns. But guaranteeing always-on functionality comes with two major challenges: heavily resource-constrained hardware; and dy…
Activity RecognitionEdge-computingSmall Data Image Classification