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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.
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)
Few-shot learning [ edit ] A prompt may include a few examples for a model to learn from, such as asking the model to complete " maison → house, chat → cat, chien →" (the expected response being dog ), [ 26 ] an approach called few-shot learning .
In 1974, a compiler with characteristics similar to the IBM implementation was created for the Digital Equipment Corporation PDP-11 computer and called WATFOR-11. The team members, Jack Schueler, Jim Welch and Terry Wilkinson, were later joined by Ian McPhee who had added new control statements to the WATFIV compiler for structured programming ...
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 ...
Consideration: Syntax, implementation, and other factors are considered. Languages like Python interpret code at runtime, whereas languages like C++ follow an approach of basing its compiler off of C's compiler. [11] Create an implementation: A first implementation is written. Compilers will convert to other formats, usually ending up as low ...
In deep learning, fine-tuning is an approach to transfer learning in which the parameters of a pre-trained neural network model are trained on new data. [1] Fine-tuning can be done on the entire neural network, or on only a subset of its layers, in which case the layers that are not being fine-tuned are "frozen" (i.e., not changed during backpropagation). [2]
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]