Papers Small Data Image Classification
“Small Data Image Classification” 태그가 달린 논문 179편 · 필터 해제
HydraMix: Multi-Image Feature Mixing for Small Data Image Classification
Training deep neural networks requires datasets with a large number of annotated examples. The collection and annotation of these datasets is not only extremely expensive but also faces legal and privacy problems. These …
Data Augmentationimage-classificationImage ClassificationSmall Data Image ClassificationImage Classification With Small Datasets: Overview and Benchmark
Image classification with small datasets has been an active research area in the recent past. However, as research in this scope is still in its infancy, two key ingredients are missing for ensuring reliable and truthf…
Classificationimage-classificationImage ClassificationSmall Data Image ClassificationChimeraMix: Image Classification on Small Datasets via Masked Feature Mixing
Deep convolutional neural networks require large amounts of labeled data samples. For many real-world applications, this is a major limitation which is commonly treated by augmentation methods. In this work, we address t…
ClassificationData Augmentationimage-classificationImage Classification+1TorchXRayVision: A library of chest X-ray datasets and models
TorchXRayVision is an open source software library for working with chest X-ray datasets and deep learning models. It provides a common interface and common pre-processing chain for a wide set of publicly available chest…
Image ClassificationMedical Image RetrievalMedical Image SegmentationMedical X-Ray Image Segmentation+3Tune It or Don't Use It: Benchmarking Data-Efficient Image Classification
Data-efficient image classification using deep neural networks in settings, where only small amounts of labeled data are available, has been an active research area in the recent past. However, an objective comparison be…
Benchmarkingimage-classificationImage ClassificationSmall Data Image ClassificationS2D2Net: An Improved Approach For Robust Steel Surface Defects Diagnosis With Small Sample Learning
Surface defect recognition of products is a necessary process to guarantee the quality of industrial production. This paper proposes a hybrid model, S2D2Net (Steel Surface Defect Diagnosis Network), for an efficient and …
Defect DetectionDiversityRobust classificationSmall Data Image Classification+1Parametric Scattering Networks
The wavelet scattering transform creates geometric invariants and deformation stability. In multiple signal domains, it has been shown to yield more discriminative representations compared to other non-learned representa…
Image ClassificationSmall Data Image ClassificationAbout Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data Annotations
Self-supervised learning methods for computer vision have demonstrated the effectiveness of pre-training feature representations, resulting in well-generalizing Deep Neural Networks, even if the annotated data are limite…
DiversityGPURepresentation LearningSelf-Supervised Learning+2Auxiliary Learning by Implicit Differentiation
Training neural networks with auxiliary tasks is a common practice for improving the performance on a main task of interest. Two main challenges arise in this multi-task learning setting: (i) designing useful auxiliary t…
Auxiliary LearningImage ClassificationImage SegmentationMulti-Task Learning+2Designing Accurate Emulators for Scientific Processes using Calibration-Driven Deep Models
Predictive models that accurately emulate complex scientific processes can achieve exponential speed-ups over numerical simulators or experiments, and at the same time provide surrogates for improving the subsequent anal…
Small Data Image ClassificationPuzzLing Machines: A Challenge on Learning From Small Data
Deep neural models have repeatedly proved excellent at memorizing surface patterns from large datasets for various ML and NLP benchmarks. They struggle to achieve human-like thinking, however, because they lack the skill…
Small Data Image ClassificationEnsemble long short-term memory (EnLSTM) network
In this study, we propose an ensemble long short-term memory (EnLSTM) network, which can be trained on a small dataset and process sequential data. The EnLSTM is built by combining the ensemble neural network (ENN) and t…
Small Data Image ClassificationST$^2$: Small-data Text Style Transfer via Multi-task Meta-Learning
Text style transfer aims to paraphrase a sentence in one style into another style while preserving content. Due to lack of parallel training data, state-of-art methods are unsupervised and rely on large datasets that sha…
Meta-LearningSentenceSmall Data Image ClassificationStyle Transfer+1Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
Recent progress in deep learning is essentially based on a "big data for small tasks" paradigm, under which massive amounts of data are used to train a classifier for a single narrow task. In this paper, we call for a sh…
Common Sense ReasoningSmall Data Image ClassificationMeta-Meta Classification for One-Shot Learning
We present a new approach, called meta-meta classification, to learning in small-data settings. In this approach, one uses a large set of learning problems to design an ensemble of learners, where each learner has high b…
ClassificationFew-Shot LearningGeneral ClassificationMeta-Learning+2Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response
Multimedia content in social media platforms provides significant information during disaster events. The types of information shared include reports of injured or deceased people, infrastructure damage, and missing or f…
Deep LearningDisaster ResponseHumanitarianInformativeness+3Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data
The possibility to use widespread and simple chest X-ray (CXR) imaging for early screening of COVID-19 patients is attracting much interest from both the clinical and the AI community. In this study we provide insights a…
Small Data Image ClassificationTransfer LearningOn Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems
In this work we present a formal theoretical framework for assessing and analyzing two classes of malevolent action towards generic Artificial Intelligence (AI) systems. Our results apply to general multi-class classifie…
Decision MakingSmall Data Image ClassificationFinding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset
Testing for COVID-19 has been unable to keep up with the demand. Further, the false negative rate is projected to be as high as 30% and test results can take some time to obtain. X-ray machines are widely available and p…
Small Data Image ClassificationGenerative Latent Implicit Conditional Optimization when Learning from Small Sample
We revisit the long-standing problem of learning from a small sample, to which end we propose a novel method called GLICO (Generative Latent Implicit Conditional Optimization). GLICO learns a mapping from the training ex…
Conditional Image GenerationData Augmentationimage-classificationImage Classification+1