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Upload file; Special pages; Permanent link; Page information; Cite this page; Get shortened URL; Download QR code; ... Few-shot learning and one-shot learning may ...
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.
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
MLIR (Multi-Level Intermediate Representation) is a unifying software framework for compiler development. [1] MLIR can make optimal use of a variety of computing platforms such as central processing units (CPUs), graphics processing units (GPUs), data processing units (DPUs), Tensor Processing Units (TPUs), field-programmable gate arrays (FPGAs), artificial intelligence (AI) application ...
CLIPS (C Language Integrated Production System) is a public-domain software tool for building expert systems.The syntax and name were inspired by Charles Forgy's OPS5.The first versions of CLIPS were developed starting in 1985 at the NASA Johnson Space Center (as an alternative for existing system ART*Inference) until 1996, when the development group's responsibilities ceased to focus on ...
LCC ("Local C Compiler" or "Little C Compiler") is a small, retargetable compiler for the ANSI C programming language. Although its source code is available at no charge for personal use, [2] it is not open-source or free software according to the usual definitions because products derived from LCC may not be sold, although components not derived from LCC may be sold. [1]
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]
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 ...