👉 Generators, a key component in the field of generative models, are algorithms designed to produce new data instances that resemble existing data sets. They operate by learning the underlying distribution of the input data, often through deep neural networks, and then generating new samples by sampling from this learned distribution. This process is crucial in applications such as image synthesis, text generation, and data augmentation, where the goal is to create realistic and diverse outputs. Generators typically consist of an encoder that maps input data into a latent space, followed by a decoder that reconstructs the output from this latent representation. Advanced techniques like attention mechanisms and variational autoencoders (VAEs) enhance the generator's ability to capture complex patterns and generate high-quality, coherent outputs.
