Small Data Image Classification
57 papers with code • 12 benchmarks • 9 datasets
Supervised image classification with tens to hundreds of labeled training examples.
Benchmarks
These leaderboards are used to track progress in Small Data Image Classification
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Datasets
CIFAR-10
CIFAR-100
CUB-200-2011
UCF-Crime
TMED
Ecoli
DEIC Benchmark
WikiChurches
Latest papers with no code
Designing 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 analysis.
PuzzLing Machines: A Challenge on Learning From Small Data
To expose this problem in a new light, we introduce a challenge on learning from small data, PuzzLing Machines, which consists of Rosetta Stone puzzles from Linguistic Olympiads for high school students.
ST$^2$: 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.
Dark, Beyond Deep: A Paradigm Shift to Cognitive AI with Humanlike Common Sense
We demonstrate the power of this perspective to develop cognitive AI systems with humanlike common sense by showing how to observe and apply FPICU with little training data to solve a wide range of challenging tasks, including tool use, planning, utility inference, and social learning.
On Adversarial Examples and Stealth Attacks in Artificial Intelligence Systems
We show that in both cases, i. e., in the case of an attack based on adversarial examples and in the case of a stealth attack, the dimensionality of the AI's decision-making space is a major contributor to the AI's susceptibility.
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.
Deep Neural Review Text Interaction for Recommendation Systems
Thus, the problem of recommendation is viewed as a text matching problem such that the matching score obtained from matching user and item texts could be considered as a good representative of their joint extent of similarity.
Adaptive 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 entities in text into predefined groups such as the names of persons, locations, quantities, organizations or percentages, etc.
Better Classifier Calibration for Small Data Sets
When the amount of data for training is limited, the traditional approach to improve calibration starts to crumble.
Meta-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.
