Patch

As explained in Deep Learning with R, Third Edition

An image patch is a localized rectangular portion of an image processed as an individual visual input unit.

An image patch is a small, localized portion of an image treated as an individual visual input unit. Convolutional layers extract patches from feature maps and apply the same transformation at many locations, helping models learn patterns such as edges and textures before combining them into larger visual concepts. section 8.1.1

Patches can be used in several closely related ways:

• Convolution uses typically 3 × 3 or 5 × 5 patches, moving them across the height and width of a feature map. section 8.1.1
• A patch includes the feature map’s channel depth: an RGB 3 × 3 patch contains values from all three color channels. section 8.1.1
• Vision Transformers split an image into a one-dimensional sequence of patches, flatten each patch into a vector, and process the resulting sequence. section 9.6
• A multimodal image encoder can turn 256 patches into vectors that act like soft tokens alongside text tokens. section 16.4.2

Figure 8.5 Valid locations of 3 × 3 patches in a 5 × 5 input feature map

Shows where 3 × 3 patches can be placed within a 5 × 5 input feature map. section 8.1.1

How Deep Learning with R, Third Edition covers it

How deep it goes

Defines patch size, channel depth, movement across feature maps, flattening into vectors, and encoding as soft tokens.

Examples

Uses 3 × 3 convolution patches, 5 × 5 feature-map placement, Vision Transformer sequences, and 256 image patches.

What it assumes

Assumes familiarity with images and tensors while introducing feature-map dimensions, channels, filters, and visual sequences.

What you can do after

Apply patches to understand local convolutional processing and image-to-sequence representations in visual models.

How it connects

Connects patches to convolution, feature maps, filters, Vision Transformers, Transformers, image encoders, and soft tokens.

liveBookmanning.com

Read Deep Learning with R, Third Edition on liveBook Get the book at manning.com

Also explained in

• 

  liveBookmanning.com
  Build a Text-to-Image Generator (from Scratch)An image patch is a spatially localized portion of an image represented separately in a grid or latent representation.
• 

  liveBookmanning.com
  Deep Learning with Python, Third EditionA local subregion of an image that is extracted and treated as one sequence element in ViT processing.
• 

  liveBookmanning.com
  Time Series Forecasting Using Foundation ModelsA patch is a segment of consecutive time-series observations grouped as one model input unit.
• 

  liveBookmanning.com
  How Large Language Models WorkA localized rectangular portion of an image converted into a numerical representation for processing by a model.

Compare how 5 books explain patch →

Related concepts

• Vision Transformerused by · A Vision Transformer is a Transformer-based neural-network architecture that processes images as sequences of tokenized regions using attention.
• PyTorch Unfoldused by · PyTorch Unfold is a tensor operation that extracts sliding local blocks from an input tensor.
• embeddingproduces · A numerical vector representation of an item or input that encodes features or relationships useful for computational tasks.
• encoderproduced by · An encoder is a component that transforms input data into an encoded representation for subsequent processing, reconstruction, comparison, or transmission.
• VQGAN decoderproduced by · A VQGAN decoder is the image-reconstruction component that maps discrete image tokens to an output image.

This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.