Like dmarx/anthology-of-machine-learning, but focused on important papers relevant to artistic applications of AI

• Deep Neural Networks are Easily Fooled: High Confidence Predictions for Unrecognizable Images

  • https://arxiv.org/pdf/1412.1897v4.pdf
  • Adversarial Images

• Deep Dream

  • https://ai.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html
  • https://ai.googleblog.com/2015/07/deepdream-code-example-for-visualizing.html

• Deconvolution and Checkerboard Artifacts

  • https://distill.pub/2016/deconv-checkerboard/

• Feature Visualization

  • https://distill.pub/2017/feature-visualization/

• The building blocks of interpretability

  • https://distill.pub/2018/building-blocks/

• Differentiable Image Parameterizations

  • https://distill.pub/2018/differentiable-parameterizations/

• Exploring Neural Networks with Activation Atlases

  • https://distill.pub/2019/activation-atlas/

• https://distill.pub/2020/circuits/

see also: https://github.com/heejkoo/Awesome-Diffusion-Models

• A Connection Between Score Matching and Denoising Autoencoders

  • http:https://www.iro.umontreal.ca/~vincentp/Publications/smdae_techreport.pdf
  • Dec 2010

• U-Net: Convolutional Networks for Biomedical Image Segmentation

  • https://arxiv.org/abs/1505.04597

• Generative Modeling by Estimating Gradients of the Data Distribution

  • https://arxiv.org/abs/1907.05600

• Improved Techniques for Training Score-Based Generative Models

  • https://arxiv.org/abs/2006.09011

• Denoising Diffusion Probabilistic Models

  • https://arxiv.org/abs/2006.11239
  • DDPM

• Denoising Diffusion Implicit Models

  • https://arxiv.org/abs/2010.02502
  • DDIM

• Improved Denoising Diffusion Probabilistic Models

  • https://arxiv.org/abs/2102.09672

• Progressive Distillation for Fast Sampling of Diffusion Models

  • https://openreview.net/forum?id=TIdIXIpzhoI
  • https://github.com/crowsonkb/v-diffusion-pytorch
  • Sept 2021

• High-Resolution Image Synthesis with Latent Diffusion Models

  • https://arxiv.org/abs/2112.10752
  • https://github.com/CompVis/latent-diffusion

• Score-Based Generative Modeling through Stochastic Differential Equations

  • https://arxiv.org/abs/2011.13456
  • https://github.com/yang-song/score_sde_pytorch
  • 10 Feb 2022

• Pseudo Numerical Methods for Diffusion Models on Manifolds

  • https://arxiv.org/abs/2202.09778
  • https://github.com/luping-liu/PNDM

• Fast Sampling of Diffusion Models with Exponential Integrator

  • https://arxiv.org/abs/2204.13902
  • https://github.com/qsh-zh/deis

• Elucidating the Design Space of Diffusion-Based Generative Models

  • https://arxiv.org/abs/2206.00364
  • https://github.com/crowsonkb/k-diffusion

• EDM2

• Cold Diffusion

• A Neural Algorithm of Artistic Style

  • https://arxiv.org/pdf/1508.06576v2.pdf

• Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

  • https://arxiv.org/abs/1703.10593v7
  • https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix
  • CycleGAN

• DiscoGAN

• Perceptual Losses for Real-Time Style Transfer and Super-Resolution

  • https://arxiv.org/abs/1603.08155v1

• Texture Networks: Feed-forward Synthesis of Textures and Stylized Images

  • https://arxiv.org/abs/1603.03417v1

• Universal Style Transfer via Feature Transforms

  • https://arxiv.org/abs/1705.08086v2

• Instance Normalization: The Missing Ingredient for Fast Stylization

  • https://arxiv.org/abs/1607.08022v3
  • https://github.com/DmitryUlyanov/texture_nets

• Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization

  • https://arxiv.org/abs/1703.06868v2
  • https://github.com/xunhuang1995/AdaIN-style

• A Style-Aware Content Loss for Real-time HD Style Transfer

  • https://arxiv.org/abs/1807.10201v2

• JoJoGAN: One Shot Face Stylization

  • https://arxiv.org/abs/2112.11641v4

• ARF: Artistic Radiance Fields

  • https://www.cs.cornell.edu/projects/arf/
  • https://arxiv.org/abs/2206.06360
  • https://github.com/Kai-46/ARF-svox2

• Artistic style transfer for videos

  • https://arxiv.org/abs/1604.08610
  • https://lmb.informatik.uni-freiburg.de/Publications/2018/RDB18/
  • https://github.com/manuelruder/artistic-videos/
  • https://github.com/manuelruder/fast-artistic-videos

• WGAN
• DCGAN
• GAN Training tricks
• spherical interpolation
• StyleGAN
• StyleGAN2
• StyleGAN2-ADA
• Projected GANs
• StyleGAN-XL
• VQGAN
• VQVAE
• RQVAE

• CLIP
• CLOOB
• Multi-lingual CLIP
• SLIP

• StyleGAN-NADA: CLIP-Guided Domain Adaptation of Image Generators

  • https://arxiv.org/abs/2108.00946
  • https://stylegan-nada.github.io/

• Aphantasia

• VQGAN + CLIP

• CFG

• DALLE

• unCLIP/DALLE2

• ruDALLE

• GLIDE

• Imagen

• Parti

• Superconditioning (twitter thread)

• z-regularization

• aesthetic models

• spherical distance

• Translation for animation effects

• Depth loss

• Flow loss

• latent consistency

• Wuerstchen

• INR
• Deep Image Prior
• SIREN
• NeRF
• Instant-NGP (Hash-NeRF)
• GIRAFFE
• GRAF
• Neural SDF
• triplane representations
• gaussian splatting

• ResNet

• EfficientNet

• Attention is All You Need

• ViT

• DETR

• MDETR

• DEiT

• DiT

• Masked Autoencoders Are Scalable Vision Learners

  • https://arxiv.org/abs/2111.06377

• LPIPS

• Frechet Inception Distance

• ArtBreeder