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Small Data Image Classification

Supervised image classification with tens to hundreds of labeled training examples.

Papers

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Showing 151–179 of 179 papers

TitleStatusHype
High-risk learning: acquiring new word vectors from tiny dataCode0
Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled DataCode0
About Explicit Variance Minimization: Training Neural Networks for Medical Imaging With Limited Data AnnotationsCode0
Unsupervised Motion Artifact Detection in Wrist-Measured Electrodermal Activity DataCode0
The Fast and the Flexible: training neural networks to learn to follow instructions from small dataCode0
Deep Kernels with Probabilistic Embeddings for Small-Data LearningCode0
Deep learning for Chemometric and non-translational dataCode0
Meta-Meta Classification for One-Shot LearningCode0
Auxiliary Learning by Implicit DifferentiationCode0
MIRA: A Computational Neuro-Based Cognitive Architecture Applied to Movie Recommender SystemsCode0
Incorporating physical constraints in a deep probabilistic machine learning framework for coarse-graining dynamical systemsCode0
Data-efficient Neural Text Compression with Interactive LearningCode0
Towards Hardware-Aware Tractable Learning of Probabilistic ModelsCode0
An Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon DrawingCode0
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and SummarisationCode0
Randomly Projected Additive Gaussian Processes for RegressionCode0
SMILES Transformer: Pre-trained Molecular Fingerprint for Low Data Drug DiscoveryCode0
A Semi-Supervised Data Augmentation Approach using 3D Graphical EnginesCode0
SSIM -A Deep Learning Approach for Recovering Missing Time Series Sensor DataCode0
Data Structures & Algorithms for Exact Inference in Hierarchical ClusteringCode0
Capturing Structure Implicitly from Time-Series having Limited DataCode0
Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin ProblemCode0
OLÉ: Orthogonal Low-Rank Embedding - A Plug and Play Geometric Loss for Deep LearningCode0
OLÉ: Orthogonal Low-rank Embedding, A Plug and Play Geometric Loss for Deep LearningCode0
Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational InferenceCode0
A physics-aware, probabilistic machine learning framework for coarse-graining high-dimensional systems in the Small Data regimeCode0
Learning to Promote Saliency DetectorsCode0
Learning What and Where to TransferCode0
On Coresets for Support Vector MachinesCode0
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