# Inception The Inception model was introduced by researchers at Google in 2014. It made use of 1x1 convolutions that are used to increase or shrink the number of channels without affecting the height and width of the image. The concept of 1x1 convolutions was introduced in the paper for the Network in Network model. A single inception block allows the network to use a combination of 1x1, 3x3, 5x5 convolutions and pooling. Inception blocks usually use 1x1 convolutions to reduce the input data volume’s size before applying 3x3 and 5x5 convolutions. The Inception model consists of multiple such inception blocks.S --- # Agent Instructions: Querying This Documentation If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question. Perform an HTTP GET request on the current page URL with the `ask` query parameter: ``` GET https://vikram-bajaj.gitbook.io/deep-learning-specialization-coursera/main-10/convolutional-neural-networks/deep-convolutional-models-case-studies/inception.md?ask= ``` The question should be specific, self-contained, and written in natural language. The response will contain a direct answer to the question and relevant excerpts and sources from the documentation. Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.