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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]
Each image is a 256×256 RGB image, divided into 32×32 patches of 4×4 each. Each patch is then converted by a discrete variational autoencoder to a token (vocabulary size 8192). [22] DALL-E was developed and announced to the public in conjunction with CLIP (Contrastive Language-Image Pre-training). [23]
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).
Contrastive Hebbian learning; Contrastive Language-Image Pre-training; Convolutional deep belief network; Convolutional layer; COTSBot; Cover's theorem; D.
Revealed in 2021, CLIP (Contrastive Language–Image Pre-training) is a model that is trained to analyze the semantic similarity between text and images. It can notably be used for image classification.
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]
Image models are commonly trained with contrastive learning or diffusion training objectives. For contrastive learning, images are randomly augmented before being evaluated on the resulting similarity of the model's representations. For diffusion models, images are noised and the model learns to gradually de-noise via the objective.
Claude (language model) Cognitive robotics; Concept drift; Conditional random field; Confusion matrix; Contrastive Language-Image Pre-training; Cost-sensitive machine learning; Coupled pattern learner; Cross-entropy method; Cross-validation (statistics) Curse of dimensionality