Master CNN, RNN, GAN, and Transformer Architectures in One Guide

This article aims to introduce the most common deep‑learning architectures used for image and natural‑language processing tasks, namely Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Generative Adversarial Networks (GAN), Transformers, and the encoder‑decoder framework.

CNN (Convolutional Neural Network)

CNN is a neural network designed for data with a grid‑like topology such as images or video. It can be imagined as a stack of filters that progressively extract higher‑level features.

Convolutional layer: Slides filters over the image, computes dot products, and produces feature maps that highlight specific patterns.

Pooling layer: Down‑samples feature maps (most often max‑pooling) to reduce spatial dimensions, lower computation, and mitigate over‑fitting.

Fully‑connected layer: Flattens the output of the previous layers and connects every neuron to the next layer, enabling the final prediction (e.g., digit classification).

In short, CNN processes structured visual data by applying a series of filters, pooling to compress representations, and fully‑connected layers for final inference.

RNN (Recurrent Neural Network)

RNN handles sequential data such as time series, speech, or text. It works like a conveyor belt that processes one element at a time while retaining information from the previous step.

Input layer: Receives each element of the sequence (e.g., a word).

Recurrent layer: Contains neurons with self‑connections that “remember” the previous hidden state, allowing information to flow across time steps.

Output layer: Generates predictions based on the current input and the stored hidden state (e.g., the next word).

RNN therefore excels at tasks that require memory of earlier elements, such as language translation, speech recognition, and time‑series forecasting.

GAN (Generative Adversarial Network)

GAN consists of two neural networks—a generator and a discriminator—that compete with each other. The generator creates synthetic data from random noise, while the discriminator judges whether a sample is real or fake.

Generator: Takes a random vector as input and learns to produce realistic samples (images, audio, text) by minimizing a loss that measures the distance to real data.

Discriminator: Receives a sample and outputs a probability of it being real, learning to distinguish generated data from authentic data.

The adversarial training continues until the generator produces high‑quality data that the discriminator can no longer reliably separate from real data.

GANs are widely applied to image/video synthesis, music generation, and text‑to‑image creation.

Transformers

Transformers, introduced in the 2017 paper “Attention Is All You Need,” are now the dominant architecture for NLP tasks such as translation, classification, and question answering.

Each Transformer layer contains two main components:

Self‑attention mechanism: Assigns a weight to every token in the input sequence, allowing the model to capture relationships between distant words without recurrent or convolutional operations.

Feed‑forward neural network: A multi‑layer perceptron that processes the output of the self‑attention sub‑layer.

The self‑attention design gives Transformers high computational efficiency on long sequences and superior performance on a variety of NLP benchmarks.

In essence, Transformers decompose text into smaller fragments, model the interactions between fragments via self‑attention, and generate coherent responses.

Encoder‑Decoder Architecture

The encoder‑decoder framework is popular for sequence‑to‑sequence problems such as machine translation. It consists of an encoder that converts the source sequence into a compact representation (often called a context vector) and a decoder that generates the target sequence from that representation.

Encoder: Can be an RNN or a Transformer; it processes the input text and outputs a context vector that captures syntax, semantics, and overall meaning.

Decoder: Also an RNN or Transformer; during training it receives the true target sequence, and during inference it generates tokens one by step, conditioned on previously generated tokens and the context vector.

The encoder‑decoder model thus enables end‑to‑end translation by learning to map an input language to an output language.

Conclusion

Understanding the characteristics, components, and typical applications of CNN, RNN, GAN, Transformers, and encoder‑decoder architectures equips practitioners with the knowledge to select the most suitable model for a given computer‑vision or NLP task.