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Classification, one-shot learning: 2015 [143] [144] American Association for the Advancement of Science: MNIST database: Database of handwritten digits. Hand-labeled. 60,000 Images, text Classification 1994 [145] [146] National Institute of Standards and Technology: Optical Recognition of Handwritten Digits Dataset Normalized bitmaps of ...
The first paper to use Caltech 101 was an incremental Bayesian approach to one-shot learning, [4] an attempt to classify an object using only a few examples, by building on prior knowledge of other classes. The Caltech 101 images, along with the annotations, were used for another one-shot learning paper at Caltech. [5]
One-shot learning is an object categorization problem, found mostly in computer vision.Whereas most machine learning-based object categorization algorithms require training on hundreds or thousands of examples, one-shot learning aims to classify objects from one, or only a few, examples.
The name is a play on words based on the earlier concept of one-shot learning, in which classification can be learned from only one, or a few, examples. Zero-shot methods generally work by associating observed and non-observed classes through some form of auxiliary information, which encodes observable distinguishing properties of objects. [1]
Photorealistic retinal images and vessel segmentations. Public domain. 2500 images with 1500*1152 pixels useful for segmentation and classification of veins and arteries on a single background. 2500 Images Classification, Segmentation 2020 [261] C. Valenti et al. EEG Database Study to examine EEG correlates of genetic predisposition to alcoholism.
CLIP can perform zero-shot image classification tasks. This is achieved by prompting the text encoder with class names and selecting the class whose embedding is closest to the image embedding. For example, to classify an image, they compared the embedding of the image with the embedding of the text "A photo of a {class}.", and the {class} that ...
The CIFAR-10 dataset (Canadian Institute For Advanced Research) is a collection of images that are commonly used to train machine learning and computer vision algorithms. It is one of the most widely used datasets for machine learning research. [1] [2] The CIFAR-10 dataset contains 60,000 32x32 color images in 10 different classes. [3]
Few-shot learning and one-shot learning may refer to: Few-shot learning, a form of prompt engineering in generative AI; One-shot learning (computer vision)