Description: Implicit models are a type of generative model that do not explicitly define a probability distribution but can generate samples that resemble the training data. Unlike explicit models, which clearly and mathematically describe how data is distributed, implicit models learn to capture the underlying characteristics of the data without providing a probability density function. This allows them to be more flexible and adaptive, resulting in a superior capacity to model complex distributions. Implicit models are particularly useful in situations where data is high-dimensional or has complicated structures that are difficult to model with traditional approaches. Their relevance has grown in the field of machine learning, where they are used in tasks such as image generation, natural language processing, and audio synthesis. By not relying on an explicit representation of the distribution, these models can explore a broader solution space, allowing them to generate more varied and creative results.
History: Implicit models have evolved over the years, especially with the rise of Generative Adversarial Networks (GANs) introduced by Ian Goodfellow and his colleagues in 2014. This approach revolutionized the way data is generated, allowing models to learn in an unsupervised manner and generate high-quality samples. Since then, various variants and architectures of implicit models have been developed, expanding their application across multiple domains.
Uses: Implicit models are used in various applications, including image generation, voice synthesis, text creation, and data quality enhancement. Their ability to learn in an unsupervised manner makes them ideal for tasks where data is scarce or difficult to label.
Examples: A notable example of an implicit model is GANs, which have been used to create realistic images of non-existent people. Another example is the use of diffusion models in audio and music generation, where they learn from large audio datasets to create new compositions.
