핵심 개념
The intersection of creativity and machine learning is explored through computational creativity theories, generative deep learning, and automatic evaluation methods.
초록
Introduction
Lady Lovelace's objection highlights the historical connection between creativity and machines.
Computational Creativity emerges as a specialized field in computer science.
Defining Creativity
Creativity is studied from four perspectives: person, press, process, and product.
Boden's three criteria for studying machine creativity are discussed.
Generative Models
Vast computational power drives breakthroughs in generative deep learning technologies.
Different forms of creativity (combinatorial, exploratory, transformational) are identified.
Variational Auto-Encoders
Core concepts of VAEs explained along with examples of models like 𝛽-VAE and VAE-GAN.
Applications
VAEs used for semi-supervised classification, iterative reasoning about objects in a scene, and latent dynamics modeling.
Critical Discussion
Evaluation of VAE models in terms of exploratory creativity and limitations in achieving novelty.
Generative Adversarial Networks
GAN architecture detailed with examples like InfoGAN and BiGAN.
Applications
GANs applied to semi-supervised learning, generating adversarial examples, recommender systems, anime design, 3D object modeling.
Critical Discussion
Evaluation of GANs' outputs in terms of appreciativeness but lack of guarantee for novelty or surprise.
Sequence Prediction Models
Autoregressive sequence prediction models explained with examples like Char-RNN and MusicVAE.
Applications
Sequence prediction models used for narrative generation, music composition, image generation based on text prompts.
Critical Discussion
Sequence prediction models' outputs characterized by exploratory creativity but lack guarantee for value or novelty.
Transformer-Based Models
Transformer architecture overview with examples like BERT and GPT family models.
Applications
Transformers widely used in NLP tasks including text summarization, generation; also applied to music generation and video-making.
Critical Discussion
Transformers induce a broader conceptual space allowing for higher-quality outputs but no guarantee for value or novelty.
Diffusion Models
Diffusion models detailed with Denoising Diffusion Probabilistic Model (DDPM) as an example.
Applications
Diffusion models used primarily for image generation but also extended to audio generation and text-to-image tasks.
Critical Discussion
Diffusion models exhibit exploratory creativity by randomly sampling from latent space without guaranteeing value or novelty.
Reinforcement Learning-Based Methods
RL-based methods explained where training relies on maximizing rewards to impose desired behavior on generative models.
Examples of Models
ORGAN model introduced as an example trained using RL to adapt GANs to sequential tasks or fine-tune pre-trained models.
통계
"Vast computational power" has led to breakthroughs in generative deep learning technologies.
"Boden's three criteria" are widely adopted for studying machine creativity.
"Combinatorial creativity" involves making unfamiliar combinations of familiar ideas using VAEs.
"GAN architecture" consists of two networks: a generative model and a discriminative model.
인용구
"The goal of this survey is to present the state-of-the-art in generative deep learning from the point of view of machine creativity."
"In fact, the goal of generative deep learning is to produce synthetic data that closely resemble real ones fed in input."