Assertion: neural network's input | /r/HasProperty | image
Facets:
value: in such a case
type: OTHER
Source sentences:
text: In the output layer, we return the class scores, say if the given input is an image having the number “3," then in the output layer the corresponding neuron “3” has a higher class score in comparison to the other neurons.
text: So we have a single input – the image, and a series or sequence of words as output.
text: One input to multiple outputs - as in image recognition, image described with words; Several contributions to one output - as in sentiment analysis, where the text is interpreted as positive or negative; Many to many - as in machine translation, where the word of the text is translated according to the context they represent as a whole;
text: The initial inputs are external data, such as images and documents.
text: The initial inputs are external data, such as images and documents.
text: In such a case, the initial inputs would be images, made up of pixels.
text: ConvNet architectures make the explicit assumption that the inputs are images, which allows us to encode certain properties into the architecture.
text: In its core, the algorithm is designed to recognize one unit of input (the image) into multiple groups of output (the description of the image).