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Explaining Generative AI: Autoregressive Models
What is an Autoregressive Model? The Basic Concept of Autoregressive Models An Autoregressive Model (AR Model) is a type of model used in time series data and other sequential data tasks, where current data points are predicted or genera... -
Explaining Generative AI: Reinforcement Learning for Generative Models
What is Reinforcement Learning for Generative Models? The Basic Concept of Reinforcement Learning for Generative Models Reinforcement learning for generative models combines the principles of reinforcement learning (RL) with generative m... -
Explaining Generative AI: Meta Generation
メタ生成とは何か メタ生成の基本概念 メタ生成（Meta Generation）は、生成AIにおける高度な技術で、生成モデルそのものを生成する、または生成プロセスを最適化する手法を指します。つまり、単にデータを生成するだけでなく、生成の方法やモデルの構造、... -
Explaining Generative AI: Beam Search
What is Beam Search? The Basic Concept of Beam Search Beam Search is a search algorithm widely used in generative AI and natural language processing (NLP) tasks, especially for sequence generation tasks such as text or speech generation.... -
Explaining Generative AI: Personalized Generation
What is Personalized Generation? The Basic Concept of Personalized Generation Personalized generation refers to the technology in generative AI that creates content tailored to individual users based on their preferences, behavior histor... -
Explaining Generative AI: Transformer Models
What Are Transformer Models? The Basic Idea Behind Transformer Models Transformer models revolutionized the field of neural networks when Google researchers introduced them in 2017. They've largely replaced traditional methods like RNNs ... -
Explaining Generative AI: Topic Modeling for Generative AI
What is Topic Modeling? The Basic Concept of Topic Modeling Topic modeling is a technique used to automatically identify hidden topics (or themes) within large volumes of text data and generate new text based on these topics. The model a... -
Explaining Generative AI: Data Augmentation for Generative AI
What is Data Augmentation? The Basic Concept of Data Augmentation Data augmentation is a technique used to artificially expand the size of a dataset by generating new data from existing data. This approach is commonly used in machine lea... -
Explaining Generative AI: Zero-Shot Generation
What is Zero-Shot Generation? The Basic Concept of Zero-Shot Learning and Zero-Shot Generation Zero-Shot Generation is a technique in generative AI that builds on the principles of zero-shot learning. Zero-shot learning refers to a model... -
Explaining Generative AI: VAE (Variational Autoencoder)
What is VAE? The Basic Concept of VAE VAE (Variational Autoencoder) is a type of generative AI model that learns the latent structure of data to generate new data. VAE is based on autoencoders, a type of neural network, but it incorporat... -
Explaining Generative AI: Transformer Decoder Models
What is a Transformer Decoder Model? The Basic Concept of Transformer Architecture The Transformer architecture is a widely used model in natural language processing (NLP) that leverages self-attention mechanisms to efficiently capture d... -
Explaining Generative AI: StyleGAN
What is StyleGAN? The Basic Concept of StyleGAN StyleGAN is a type of Generative Adversarial Network (GAN) specifically developed for generating high-quality images. Introduced by NVIDIA in 2018, StyleGAN gained significant attention, pa... -
Explaining Generative AI: GAN (Generative Adversarial Networks)
What is GAN? The Basic Concept of GAN GAN (Generative Adversarial Networks) is a type of generative AI introduced by Ian Goodfellow and his colleagues in 2014. GANs consist of two neural networks, known as the "generator" and the "discri... -
Explaining Generative AI: Few-shot Learning
What is Few-shot Learning? The Basic Concept of Few-shot Learning Few-shot Learning is a machine learning technique designed to train models using only a small amount of data (few shots) while achieving high performance on new tasks. Unl... -
Explaining Generative AI: Diffusion Models
What is a Diffusion Model? The Basic Concept of Diffusion Models A Diffusion Model is a type of generative model that utilizes probabilistic processes to generate data. It is particularly effective in high-quality data generation and noi...
