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  2. Contrastive Language-Image Pre-training - Wikipedia

    en.wikipedia.org/wiki/Contrastive_Language-Image...

    CLIP has been used as a component in multimodal learning. For example, during the training of Google DeepMind's Flamingo (2022), [34] the authors trained a CLIP pair, with BERT as the text encoder and NormalizerFree ResNet F6 [35] as the image encoder. The image encoder of the CLIP pair was taken with parameters frozen and the text encoder was ...

  3. Self-supervised learning - Wikipedia

    en.wikipedia.org/wiki/Self-supervised_learning

    Contrastive self-supervised learning uses both positive and negative examples. The loss function in contrastive learning is used to minimize the distance between positive sample pairs, while maximizing the distance between negative sample pairs. [9] An early example uses a pair of 1-dimensional convolutional neural networks to process a pair of ...

  4. Picture superiority effect - Wikipedia

    en.wikipedia.org/wiki/Picture_superiority_effect

    Allan Paivio's dual-coding theory is a basis of picture superiority effect. Paivio claims that pictures have advantages over words with regards to coding and retrieval of stored memory because pictures are coded more easily and can be retrieved from symbolic mode, while the dual coding process using words is more difficult for both coding and retrieval.

  5. Contrastive analysis - Wikipedia

    en.wikipedia.org/wiki/Contrastive_analysis

    Hence, more tailor-made language design can be adopted; examples include awareness raising teaching method and hierarchical learning teaching curriculum. Second language learning: Awareness raising is the major contribution of CA in second language learning. This includes CA's abilities to explain observed errors and to outline the differences ...

  6. Adaptive histogram equalization - Wikipedia

    en.wikipedia.org/wiki/Adaptive_histogram...

    Adaptive histogram equalization (AHE) is a computer image processing technique used to improve contrast in images. It differs from ordinary histogram equalization in the respect that the adaptive method computes several histograms, each corresponding to a distinct section of the image, and uses them to redistribute the lightness values of the image.

  7. Contrastive Hebbian learning - Wikipedia

    en.wikipedia.org/wiki/Contrastive_Hebbian_learning

    Contrastive Hebbian learning is a biologically plausible form of Hebbian learning. It is based on the contrastive divergence algorithm, which has been used to train a variety of energy-based latent variable models. [1] In 2003, contrastive Hebbian learning was shown to be equivalent in power to the backpropagation algorithms commonly used in ...

  8. Contrastive linguistics - Wikipedia

    en.wikipedia.org/wiki/Contrastive_linguistics

    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.

  9. Bag-of-words model - Wikipedia

    en.wikipedia.org/wiki/Bag-of-words_model

    The BoW representation of a text removes all word ordering. For example, the BoW representation of "man bites dog" and "dog bites man" are the same, so any algorithm that operates with a BoW representation of text must treat them in the same way. Despite this lack of syntax or grammar, BoW representation is fast and may be sufficient for simple ...