03 月

• [x] 2016-PCS-End-to-end optimization of nonlinear transform codes for perceptual quality
• [x] 2016-ICLR-Density Modeling of Images using a Generalized Normalization Transformation

04 月

• [x] 2017-ICLR-End-to-end Optimized Image Compression
• [x] 2018-ICLR-Variational image compression with a scale Hyper-prior
• [x] 2016-CVPR-Deep Residual Learning for Image Recognition
• [x] 2021-Arxiv-Learning Transferable Visual Models From Natural Language Supervision

05 月

• [x] 2017-CVPR-PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
• [x] 2017-NIPS-PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• [x] 2022-CVPR-Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
• [x] 2021-ICLR-An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale
• [x] 2014-NIPS-Generative Adversarial Nets
• [x] 2017-NIPS-Attention Is All You Need

06 月

• [x] 2014-NIPS-Two-Stream Convolutional Networks for Action Recognition in Videos
• [x] 2020-ECCV-End-to-End Object Detection with Transformers
• [x] 2018-NIPS-Joint autoregressive and hierarchical priors for learned image compression
• [ ] 2016-NIPS-Conditional Image Generation with PixelCNN Decoders
• [ ] 2018-ICIP-Image-Dependent Local Entropy Models for Learned Image Compression
• [ ] 2017-ICLR-Lossy Image Compression with Compressive Autoencoders
• [ ] 2018-CVPR-Improved Lossy Image Compression with Priming and Spatially Adaptive Bit Rates for Recurrent Networks
• [ ] 2022-CVPR-ELIC: Efficient Learned Image Compression with Unevenly Grouped Space-Channel Contextual Adaptive Coding
• [x] 2019-ICLR-Integer Networks for Data Compression with Latent-Variable Models
• [ ] 2022-CVPR-PILC: Practical Image Lossless Compression with an End-to-end GPU Oriented Neural Framework

07 月

• [x] 2016-ICML-Pixel Recurrent Neural Networks
• [ ] 2016-NIPS-Conditional Image Generation with PixelCNN Decoders
• [x] 2020-MM-Instability of Successive Deep Image Compression
• [x] 2020-Arxiv-Implicit Feature Pyramid Network for Object Detection
• [ ] 2019-ICML-Similarity of Neural Network Representations Revisited
• [ ] 2022-Arxiv-Hybrid Spatial-Temporal Entropy Modelling for Neural Video Compression
• [ ] 2020-CVPRW-Variable Rate Image Compression Method with Dead-zone Quantizer
• [ ] 2019-NIPS-Deep Equilibrium Models
• [ ] 2019-AAAI-Recurrent Stacking of Layers for Compact Neural Machine Translation Models
• [ ] 2022-Arxiv-Denial-of-Service Attacks on Learned Image Compression
• [ ] 2020-NIPS-Multiscale Deep Equilibrium Models
• [ ] 2021-ICML-Stabilizing Equilibrium Models by Jacobian Regularization
• [ ] 2018-NIPS-Neural Ordinary Differential Equations
• [ ] 2019-ICLR-FFJORD: Free-form Continuous Dynamics for Scalable Reversible Generative Models

08 月

• [ ] 2019-NIPS-Deep Equilibrium Models
• [ ] 2020-NIPS-Monotone operator equilibrium networks
• [ ] 2021-SIAM-Implicit Deep Learning
• [ ] 2019-Arixv-Deep Declarative Networks: A New Hope
• [ ] 2022-ICML-CerDEQ: Certifiable Deep Equilibrium Model
• [ ] 2022-ICML-Variational Inference for Infinitely Deep Neural Networks
• [ ] 2021-TCI-Deep Equilibrium Architectures for Inverse Problems in Imaging
• [ ] 2022-ICML-Deep equilibrium networks are sensitive to initialization statistics
• [ ] 2022-ICML-CerDEQ: Certifiable Deep Equilibrium Model
• [ ] 2021-Arxiv-Stabilizing Equilibrium Models by Jacobian Regularization
• [ ] 2022-ICLR-Optimization inspired Multi-Branch Equilibrium Models