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The naming convention for these models often reflects the specific ViT architecture used. For instance, "ViT-L/14" means a "vision transformer large" (compared to other models in the same series) with a patch size of 14, meaning that the image is divided into 14-by-14 pixel patches before being processed by the transformer.
Contrastive linguistics, since its inception by Robert Lado in the 1950s, has often been linked to aspects of applied linguistics, e.g., to avoid interference errors in foreign-language learning, as advocated by Di Pietro (1971) [1] (see also contrastive analysis), to assist interlingual transfer in the process of translating texts from one ...
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).
DALL-E was developed and announced to the public in conjunction with CLIP (Contrastive Language-Image Pre-training). [23] CLIP is a separate model based on contrastive learning that was trained on 400 million pairs of images with text captions scraped from the Internet. Its role is to "understand and rank" DALL-E's output by predicting which ...
Grammatical abbreviations are generally written in full or small caps to visually distinguish them from the translations of lexical words. For instance, capital or small-cap PAST (frequently abbreviated to PST) glosses a grammatical past-tense morpheme, while lower-case 'past' would be a literal translation of a word with that meaning.
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".
A contrastive distribution in linguistics is a relationship between two or more different elements which can appear in the same context, but cause a change in meaning when one is substituted for another in that context. A contrastive distribution is demonstrated with a minimal pair.
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]