Formatting Instructions for ICLR2020 Conference Submissions

Papers with code
Hugging Face
labml.ai Deep Learning Paper Implementations
PyTorch Image Models

How PyTorch Transposed Conv1D Work
Deconvolution and Checkerboard Artifacts
Flow-based Deep Generative Models



Transformer Language Model Mathematical Definition
Transformerの最前線 ~ 畳込みニューラルネットワークの先へ ~
Transformer メタサーベイ

【Flow-based Model】
Normalizing Flow入門 第1回 変分推論
Normalizing Flow入門 第2回 Planar Flow
Normalizing Flow入門 第3回 Bijective Coupling
Normalizing Flow入門 第4回 Glow
Normalizing Flow入門 第5回 Autoregressive Flow
Normalizing Flow入門 第6回 Residual Flow
Normalizing Flow入門 第7回 Neural ODEとFFJORD

Variational Inference with Normalizing Flows
NICE: Non-linear Independent Components Estimation
Density estimation using Real NVP
Glow: Generative Flow with Invertible 1x1 Convolutions

【Diffusion Model】
Awesome Diffusion Models
Denoising Diffusion Probabilistic Models (DDPM)
Understanding Diffusion Models: A Unified Perspective
Generative Modeling by Estimating Gradients of the Data Distribution
Inject Noise to Remove Noise: A Deep Dive into Score-Based Generative Modeling Techniques


Large Scale GAN Training for High Fidelity Natural Image Synthesis
A Style-Based Generator Architecture for Generative Adversarial Networks
HoloGAN: Unsupervised Learning of 3D Representations from Natural Images
Few-Shot Adversarial Learning of Realistic Neural Talking Head Models
SinGAN: Learning a Generative Model from a Single Natural Image
Towards a Deeper Understanding of Adversarial Losses under a Discriminative Adversarial Network Setting

●Generative Adversarial Networks
Generative Adversarial Nets
Adversarial Feature Learning
Adversarially Learned Inference
Adversarially Learned Inference - GitHub Pages
Autoencoding beyond pixels using a learned similarity metric
●Adversarial Autoencoder
Adversarial Autoencoders
●Wasserstein GAN
Wasserstein GAN
●Gradient Penalty
Improved Training Wasserstein GANs
●Perceptual Loss
Perceptual Loss for Real-Time Style Transfer and Super-Resolution
●Hinge Loss
Hierarchical Implicit Models and Likelihood-Free Variational Inference Tran, Ranganath, Blei, 2017
Geometric GAN Lim & Ye, 2017
Spectral Normalization for Generative Adversarial Networks Miyato, Kataoka, Koyama, Yoshida, 2018
●Feature Matching Loss
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
●Instance Normalization
Instance Normalization: The Missing Ingredient for Fast Stylization
●Spectral Normalization
Spectral Normalization for Generative Adversarial Networks
●Adaptive Instance Normalization(AdaIN)
Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization
Self-Attention Generative Adversarial Networks
●Projection Discriminator
cGANs with Projection Discriminator
●Inception Score
Improved Techniques for Training GANs
●Frechet-Inception Distance(FID)
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
●Structured Similarity(SSIM)
Image Quality Assessment: From Error Visibility to Structural Similarity
●Perception-Distortion Tradeoff
The Perception-Distortion Tradeoff
●Cosine Similarity(CSIM)
ArcFace: Additive Angular Margin Loss for Deep Face Recognition

【Meta Learning】
Meta-Learning: Learning to Learn Fast - Lil'Log
Few-shot Learningとは何なのか【Generalizing from a few examples: A survey on few-shot learning】
MetaGAN: An Adversarial Approach to Few-Shot Learning
Model-Agnostic Meta-Learning For Fast Adaptation of Deep Neural Networks
●Reptile (FOMAML)
On First-Order Meta-Learning Algorithims
●Implicit MAML
Meta-Learning with Implicit Graidents
Modular Meta-Learning with Shrinkage
Fast Context Adaptation via Meta-Learning
Task-Agnostic Meta-Learning for Few-shot Learning

【The Lottery Ticket Hypothesis】
The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks
What's Hidden in a Randomly Weighted Neural Network?
Training BatchNorm and Only BatchNorm: On the Expressive Power of Random Feature in CNNs