【ICCV2021教程】基于能量生成模型的理论与应用,193页ppt

Part I : 基础 Fundamentals 

1. Background

• Probabilistic models of images

• Gibbs distribution in statistical physics

• Filters, Random Fields and Maximum Entropy (FRAME) models

• Generative ConvNet: EBM parameterized by modern neural network

2. Elements of Energy-Based Generative Learning

• Understanding Kullback-Leibler divergences

• Maximum likelihood learning, analysis by synthesis

• Gradient-based MCMC and Langevin sampling

• Adversarial self-critic interpretations

• Short-run MCMC for synthesis for EBMs

• Equivalence between EBMs and discriminative models

Part II : 进展 Advanced

1. Strategy for Efficient Learning and Sampling

• Multi-stage expanding and sampling for EBMs

• Multi-grid learning and sampling for EBMs

• Learning EBM by recovery likelihood

2. Energy-Based Generative Frameworks

• Generative cooperative network

• Divergence triangle

• Latent Space Energy-Based Prior Model

• Flow contrastive estimation of energy-based model

Part III : 应用 Applications

1. Energy-Based Generative Neural Networks

• Generative ConvNet: EBMs for images

• Spatial-Temporal Generative ConvNet: EBMs for videos

• Generative VoxelNet: EBMs for 3D volumetric shapes

• Generative PointNet: EBMs for unordered point clouds

• EBMs for inverse optimal control and trajectory prediction

• Patchwise Generative ConvNet: EBMs for internal learning

2. Energy-Based Generative Cooperative Networks

• Unconditioned image, video, 3D shape synthesis

• Supervised conditional learning

• Unsupervised image-to-image translation

• Unsupervised sequence-to-sequence translation

• Generative saliency prediction

3. Latent Space Energy-Based Models

• Text Generation

• Molecule Generation

• Anomaly Detection

• Saliency prediction using transformer with energy-based prior

• Trajectory Prediction

• Semi-Supervised Learning

• Controlled Text Generation