Schedule

Reading List:

• Szeliski Book, Chapters 3.1, 3.2, and 3.5
• Color Transfer between Images
• Non-rigid dense correspondence with applications for image enhancement

Reading list:

• Szeliski Book, Chapter 3.6
• Image Deformation using Moving Least Squares, Schaefer et al. in TOG, 2006

Reading list:

• Poisson Image Editing, Pérez et al. in SIGGRAPH, 2003
• Scene Completion using Millions of Photographs, Hays et al. in TOG, 2007
• CG2Real: Improving the Realism of Computer Generated Images using a Large Collection of Photographs, Johnson et al. in TVCG, 2010
• Modeling the shape of the scene: A holistic representation of the spatial envelope, Oliva et al. in IJCV, 2001
• Semantic photo synthesis, Johnson et al. in Computer Graphics Forum, 2006
• Sketch2Photo: internet image montage, Chen et al. in SIGGRAPH, 2019
• Photo Clip Art, Lalonde et al. in SIGGRAPH, 2007
• ShadowDraw: Real-Time User Guidance for Freehand Drawing, Lee et al. in SIGGRAPH, 2011
• AverageExplorer: Interactive Exploration and Alignment of Visual Data Collections, Zhu et al. in SIGGRAPH, 2014
• Image Deformation Using Moving Least Squares, Schaefer et al. in SIGGRAPH, 2006

Reading list:

• Deep Learning Book, Chapter 6 and 9.
• Szeliski Book, Chapter 5.3 and 5.4.
• Gradient-based learning applied to document recognition, Lecun et al., Proc of IEEE, 1998.
• Learning to Generate Chairs, Tables and Cars with Convolutional Networks, Dosovitskiy et al., PAMI 2017 (CVPR 2015)
• Deconvolution and Checkerboard Artifacts
• Colorful Image Colorization, Zhang et al., ECCV 2016.
• Notes on Backpropagation and CNNs: Olah and 231n

Reading List:

• Szeliski Book, Chapter 5.5.3
• Murphy Book, Chapter 26
• Generative Adversarial Networks, Goodfellow et al., NeurIPS 2014.
• GAN Tutorial (NeurIPS 2016), by Ian Goodfellow.
• ICCV 2017 Tutorial on GANs.
• CVPR 2018 Tutorial on GANs.
• Image style transfer using convolutional neural networks, Gatys et al., CVPR 2016.
• Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Johnson et al., ECCV 2016.
• Generating images with perceptual similarity metrics based on deep networks. Dosovitskiy and Brox. NeurIPS, 2016
• The Unreasonable Effectiveness of Deep Features as a Perceptual Metric. Zhang et al., CVPR 2018

Reading List:

• Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks, Radford et al., ICLR 2016
• Improved Training of Wasserstein GANs, Gulrajani et al., NeurIPS 2017
• Least Squares Generative Adversarial Networks, Mao et al. 2017
• Progressive Growing of GANs for Improved Quality, Stability, and Variation, Karras et al, ICLR 2018
• Spectral Normalization for Generative Adversarial Networks, Miyato et al., ICLR 2018
• A Style-Based Generator Architecture for Generative Adversarial Networks, Karras et al., CVPR 2019
• Analyzing and Improving the Image Quality of StyleGAN, Karras et al., 2020
• Alias-Free Generative Adversarial Networks, Karras et al., 2021
• Training Generative Adversarial Networks with Limited Data, Karras et al., 2020
• Differentiable Augmentation for Data-Efficient GAN Training, Zhao et al., 2020
• Image Manipulation with Perceptual Discriminators, Sungatullina et al., ECCV 2018.
• Projected GANs Converge Faster, Sauer et al., 2021
• Ensembling Off-the-shelf Models for GAN Training, Kumari et al., 2021

Reading List:

• Deep Learning Book, Chapter 20
• Murphy Book, Chapter 21 and 22
• Auto-Encoding Variational Bayes, Kingma and Welling, 2013
• Autoencoding beyond pixels using a learned similarity metric, Larsen et al, CVPR 2016
• Conditional Image Generation with PixelCNN Decoders, Oord et al, 2016
• Generating Diverse High-Fidelity Images with VQ-VAE-2, Razavi et al., 2019
• Taming Transformers for High-Resolution Image Synthesis, Esser et al, 2021
• How to Train Your Energy-Based Models, Song and Kingma, 2021

Reading List:

• Murphy Book, Chapter 22 and 25
• Masked Autoencoders Are Scalable Vision Learners, He et al., 2021
• MaskGIT: Masked Generative Image Transformer, Chang et al., 2022
• Scalable Image Generation via Next-Scale Prediction, Tian et al., 2024
• Denoising Diffusion Probabilistic Models, Ho et al., 2020
• Score-Based Generative Modeling through Stochastic Differential Equations (SDE), Song et al., ICLR 2021

Reading List:

• Murphy Book, Chapter 25
• Denoising Diffusion Probabilistic Models, Ho et al., 2020
• Denoising Diffusion Implicit Models, Song et al., 2021
• Score-Based Generative Modeling through Stochastic Differential Equations (SDE), Song et al., ICLR 2021

Reading List:

• Generating Diverse High-Fidelity Images with VQ-VAE-2 (VQ-VAE-2)
• Taming Transformers for High-Resolution Image Synthesis (VQGAN)
• Generative Pretraining from Pixels (ImageGPT)
• MaskGIT: Masked Generative Image Transformer (MaskGIT)
• Scalable Image Generation via Next-Scale Prediction
• Analyzing and Improving the Image Quality of StyleGAN (StyleGAN2)
• Alias-Free Generative Adversarial Networks (StyleGAN3)
• Which Training Methods for GANs do actually Converge?
• StyleGAN-XL: Scaling StyleGAN to Large Diverse Datasets (StyleGAN-XL)
• Denoising Diffusion Implicit Models (DDIM)
• Score-Based Generative Modeling through Stochastic Differential Equations (SDE)
• Elucidating the Design Space of Diffusion-Based Generative Models
• Analyzing and Improving the Training Dynamics of Diffusion Models
• Scalable Diffusion Models with Transformers (DIT)
• Consistency Models
• Improved Techniques for Training Consistency Models
• On Distillation of Guided Diffusion Models

Reading List:

• Image-to-Image Translation with Conditional Adversarial Networks, Isola et al, CVPR 2017
• Toward Multimodal Image-to-Image Translation, Zhu et al, NeurIPS 2017
• Multi-Agent Diverse Generative Adversarial Networks, Ghosh et al, CVPR 2018
• Diversity-Sensitive Conditional Generative Adversarial Networks, Yang et al, ICLR 2019
• High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs, Wang et al, CVPR 2018.
• Semantic Image Synthesis with Spatially-Adaptive Normalization, Park et al, CVPR 2019

Reading List:

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks, Zhu et al, ICCV 2017
• Unsupervised Image-to-Image Translation Networks, Liu et al., NeurIPS 2017
• Learning from Simulated and Unsupervised Images through Adversarial Training, Shrivastava et al, CVPR 2017
• Multimodal Unsupervised Image-to-Image Translation, Huang et al., ECCV 2018
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations, Meng et al., ICLR 2022
• Adding Conditional Control to Text-to-Image Diffusion Models, Zhang et al., ICCV 2024

Reading List:

• Separating Style and Content, Tenenbaum & Freeman, Neurips 1996
• Texture Synthesis by Non-parametric Sampling, Efros and Leung, ICCV 1999
• Image Quilting for Texture Synthesis and Transfer, Efros and Freeman, SIGGRAPH 2001.
• Image Analogies, Hertzmann et al, SIGGRAPH 2001
• Deep Photo Style Transfer, Luan et al., CVPR 2017

Reading List:

• A text-to-picture synthesis system for augmenting communication, Zhu et al., AAAI 2007
• Generating Images from Captions with Attention, Mansimov et al., ICLR 2016
• Generative Adversarial Text to Image Synthesis. Reed et al., ICML 2016
• StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks. Zhang et al., ICCV 2017
• High-Resolution Image Synthesis with Latent Diffusion Models, Rombach et al., CVPR 2022
• Hierarchical Text-Conditional Image Generation with CLIP Latents, Ramesh et al., arXiv 2022
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding. Saharia, Chan, et al., NeurIPS 2022
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation, Yu et al., TMLR 2022
• Scaling up GANs for Text-to-Image Synthesis, Kang et al., CVPR 2023

Reading List:

• The Perception-Distortion Tradeoff
• Image Analogies
• Controlling Perceptual Factors in Neural Style Transfer
• Visual attribute transfer through deep image analogy
• Deep Photo Style Transfer
• Zero-Shot Text-to-Image Generation (DALL·E)
• Hierarchical Text-Conditional Image Generation with CLIP Latents (DALL·E 2)
• Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding (Imagen)
• Scaling Autoregressive Models for Content-Rich Text-to-Image Generation (PARTI)
• Muse: Text-To-Image Generation via Masked Generative Transformers (MUSE)
• Scene-Based Text-to-Image Generation with Human Priors (Make-a-Scene)
• MultiDiffusion: Fusing Diffusion Paths for Controlled Image Generation
• Adding Conditional Control to Text-to-Image Diffusion Models (ControlNet)
• One-step Diffusion with Distribution Matching Distillation
• Adversarial Diffusion Distillation
• SDXL: Improving Latent Diffusion Models for High-Resolution Image Synthesis
• Scaling up GANs for Text-to-Image Synthesis
• StyleGAN-T: Unlocking the Power of GANs for Fast Large-Scale Text-to-Image Synthesis

Reading List:

• Generative Visual Manipulation on the Natural Image Manifold, Zhu et al., ECCV 2016
• Semantic Photo Manipulation with a Generative Image Prior, Bau et al., SIGGRAPH 2019
• Image2StyleGAN: How to Embed Images Into the StyleGAN Latent Space?, Abdal et al., ICCV 2019
• GANSpace: Discovering Interpretable GAN Controls, Härkönen et al., NeurIPS 2020
• Exploiting Deep Generative Prior for Versatile Image Restoration and Manipulation, Pan et al., ECCV 2020
• GAN Inversion: A Survey. Xia et al. 2021

Reading List:

• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion, Gal et al., ICLR 2023
• DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation, Ruiz et al., CVPR 2023
• Multi-Concept Customization of Text-to-Image Diffusion, Kumari et al., CVPR 2023
• LoRA: Low-Rank Adaptation of Large Language Models, Hu et al., arXiv 2021
• SVDiff: Compact Parameter Space for Diffusion Fine-Tuning, Han et al., ICCV 2023
• IP-Adapter: Text Compatible Image Prompt Adapter for Text-to-Image Diffusion Models, Ye et al., CVPR 2024
• Encoder-based domain tuning for fast personalization of text-to-image models, Gal et al., SIGGRAPH 2023

Reading List:

• Colorization Using Optimization
• A Closed Form Solution to Natural Image Matting
• PatchMatch: A Randomized Correspondence Algorithm for Structural Image Editing
• Deep Image Prior
• StyleCLIP: Text-Driven Manipulation of StyleGAN Imagery
• Encoding in style: a stylegan encoder for image-to-image translation
• Rewriting a Deep Generative Model
• DreamBooth: Fine Tuning Text-to-Image Diffusion Models for Subject-Driven Generation
• An Image is Worth One Word: Personalizing Text-to-Image Generation using Textual Inversion
• SDEdit: Guided Image Synthesis and Editing with Stochastic Differential Equations
• Prompt-to-Prompt Image Editing with Cross Attention Control
• Imagic: Text-Based Real Image Editing with Diffusion Models
• InstructPix2Pix Learning to Follow Image Editing Instructions, Brooks et al., 2022.
• Masactrl: Tuning-free mutual self-attention control for consistent image synthesis and editing
• Drag Your GAN: Interactive Point-based Manipulation on the Generative Image Manifold
• DragDiffusion: Harnessing Diffusion Models for Interactive Point-based Image Editing