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Contrastive Language-Image Pre-training (CLIP) is a technique for training a pair of neural network models, one for image understanding and one for text understanding, using a contrastive objective. [1]
Contrastive Language-Image Pre-training (CLIP) allows joint pretraining of a text encoder and an image encoder, such that a matching image-text pair have image encoding vector and text encoding vector that span a small angle (having a large cosine similarity).
The theoretical foundations for what became known as the contrastive analysis hypothesis were formulated in Robert Lado's Linguistics Across Cultures (1957). In this book, Lado claimed that "those elements which are similar to [the learner's] native language will be simple for him, and those elements that are different will be difficult".
That development led to the emergence of large language models such as BERT (2018) [28] which was a pre-trained transformer (PT) but not designed to be generative (BERT was an "encoder-only" model). Also in 2018, OpenAI published Improving Language Understanding by Generative Pre-Training, which introduced GPT-1, the first in its GPT series. [29]
Generative Pre-trained Transformer 1 (GPT-1) was the first of OpenAI's large language models following Google's invention of the transformer architecture in 2017. [2] In June 2018, OpenAI released a paper entitled "Improving Language Understanding by Generative Pre-Training", [ 3 ] in which they introduced that initial model along with the ...
This is a list of English-language words of Hindi and Urdu origin, two distinguished registers of the Hindustani language (Hindi-Urdu). Many of the Hindi and Urdu equivalents have originated from Sanskrit; see List of English words of Sanskrit origin.
While traditional linguistic studies had developed comparative methods (comparative linguistics), chiefly to demonstrate family relations between cognate languages, or to illustrate the historical developments of one or more languages, modern contrastive linguistics intends to show in what ways the two respective languages differ, in order to help in the solution of practical problems.
The language has generally been described as having contrastive word stress or accent as evidenced by numerous stem and stem–clitic minimal pairs such as /mɒhi/ [mɒ.hí] (' fish ') and /mɒh-i/ [mɒ́.hi] (' some month '). The authors argue that the reason why Persian listeners are "stress-deaf" is that their accent locations arise ...