Draft:MiniImageNet
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MiniImageNet is a streamlined subset of the larger ImageNet database, specifically curated to support research in few-shot learning. Few-shot learning is an area of machine learning focused on enabling models to effectively learn or recognize new tasks with minimal data—often only a few examples per class. This subset was introduced to help develop and evaluate algorithms designed to perform under these data-constrained scenarios.[1]
Overview[edit]
MiniImageNet selects 100 classes from the broader ImageNet dataset, with each class containing 600 images. This subset provides a well-rounded representation of various object categories, balancing the dataset to test the limits of few-shot learning models adequately.[2]
Dataset Structure[edit]
The dataset is organized into three distinct splits to facilitate a structured training and evaluation process:
- Training set: Consists of 64 classes used for training the models.
- Validation set: Comprises 16 classes used to tune and validate the models' parameters during the development phase.
- Test set: Includes 20 classes used to evaluate the models' performance on completely new data, simulating real-world applications where the model encounters unfamiliar objects.[3]
Significance in Machine Learning[edit]
MiniImageNet serves as a critical tool in the machine learning community for advancing few-shot learning technologies.[4] By providing a smaller, more focused dataset, researchers can quickly prototype and test new algorithms, making it a widely used benchmark in the field. The structure of MiniImageNet challenges models to adapt to new tasks with limited information,[5] a key ability for practical applications across various domains where extensive data collection is impractical or impossible.
Applications[edit]
The applications of few-shot learning and by extension, MiniImageNet, span several fields including robotics, where machines must recognize new objects in dynamic environments, and healthcare, where models are expected to diagnose from rare medical images. As such, MiniImageNet is not just a testbed for theoretical models but a foundational dataset for developing AI systems that must operate robustly in the real world with limited data.[1]
References[edit]
- ^ a b Liu, Chen; Xu, Chengming; Wang, Yikai; Zhang, Li; Fu, Yanwei (2020). "An Embarrassingly Simple Baseline to One-Shot Learning": 922–923.
{{cite journal}}: Cite journal requires|journal=(help) - ^ Liu, Yaoyao (2024-04-03), yaoyao-liu/mini-imagenet-tools, retrieved 2024-04-18
- ^ "What is the official way to use miniImagenet in Pytorch?". PyTorch Forums. 2020-03-24. Retrieved 2024-04-18.
- ^ semantic-task-sampling.streamlit.app https://semantic-task-sampling.streamlit.app/. Retrieved 2024-04-18.
{{cite web}}: Missing or empty|title=(help) - ^ "Papers with Code - miniImagenet Benchmark (Cross-Domain Few-Shot)". paperswithcode.com. Retrieved 2024-04-18.
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