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Attention Mechanism(also: Attention)

A technique in neural networks that allows models to focus on relevant parts of the input when generating each part of the output, rather than relying solely on a fixed-length context vector. In sequence-to-sequence models, attention computes a weighted combination of all…

Inception-v3(also: Inception v3)

A deep convolutional neural network architecture developed by Google for image recognition, introduced in 2015. It uses "inception modules" that apply multiple convolution filter sizes in parallel to efficiently capture features at different scales, balancing recognition…

Sequence-to-Sequence(also: Seq2Seq, Encoder-Decoder)

A neural network architecture designed for tasks where both input and output are sequences of variable length, such as machine translation, speech recognition, and video captioning. A seq2seq model consists of an encoder that processes the input sequence into a fixed-length…

Transformer(also: Transformer Model, Transformer Architecture)

A deep learning architecture introduced by Vaswani et al. in 2017 that relies entirely on attention mechanisms rather than recurrence (RNNs) or convolution for sequence modeling tasks. Transformers process entire input sequences in parallel using "self-attention" to weigh the…

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