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Vector embeddings: Difference between revisions
→Example: Image Embedding with a Convolutional Neural Network
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While such embeddings effectively maintain the semantic information of a pixel's neighborhood in an image, they are highly sensitive to transformations like [[shifts]], [[scaling]], [[cropping]], and other [[image manipulation]] operations. Consequently, they are often used as raw inputs to learn more robust embeddings. | While such embeddings effectively maintain the semantic information of a pixel's neighborhood in an image, they are highly sensitive to transformations like [[shifts]], [[scaling]], [[cropping]], and other [[image manipulation]] operations. Consequently, they are often used as raw inputs to learn more robust embeddings. | ||
A Convolutional Neural Network (CNN or ConvNet) is a class of deep learning | A Convolutional Neural Network (CNN or [[ConvNet]]) is a class of [[deep learning architecture]]s typically applied to visual data, transforming images into embeddings. CNNs process input through hierarchical small local sub-inputs known as receptive fields. Each neuron in each network layer processes a specific receptive field from the previous layer. Each layer either applies a convolution on the receptive field or reduces the input size through a process called subsampling. | ||
A typical CNN structure includes receptive fields as sub-squares in each layer, serving as input to a single [[neuron]] within the preceding layer. Subsampling operations reduce layer size, while convolution operations expand layer size. The resulting vector embedding is obtained through a fully connected layer. | A typical CNN structure includes receptive fields as sub-squares in each layer, serving as input to a single [[neuron]] within the preceding layer. Subsampling operations reduce layer size, while convolution operations expand layer size. The resulting vector embedding is obtained through a fully connected layer. | ||