MI2RL Paper Study

Paper Study about Deep Learning
MI2RL@Asan Medical Center, South Korea, Professor : Namkug Kim
Trello Link
Members
- Sungman Cho, Sungchul Kim, Ryoungwoo Jang, Minjee Kim, Kyunghwa Lee, Kyuri Kim, Dain Eun.

Date	Title	Presenter
2020.08.03	BYOL: Bootstrap Your Own Latent A New Approach to Self-Supervised Learning	Sungman Cho
2020.08.03	FickleNet: Weakly and Supervised Semantic Image Segmentation using Stochastic Inference	Sungchul Kim
2020.08.10	What Uncertainties Do We Need in Bayesian Deep Learning for Computer Vision?	Ryoungwoo Jang
2020.08.10	Uncertainty-Aware Weakly Supervised Action Detection from Untrimmed Videos	Minjee Kim
2020.08.31	Analyzing and Improving the Image Quality of StyleGAN	Kyunghwa Lee
2020.08.31	Dynamic Routing Between Capsules	Dain Eun
2020.09.07	A Closer Look at Few-Shot Classification	Kyuri Kim
2020.09.07	DRIT:Diver Image-to-Image Translation via Disentangled Representations	Sungman Cho
2020.09.14	UDA:Unsupervised Data Augmentation for Consistency Training	Sungchul Kim
2020.09.14	SIREN	Ryoungwoo Jang
2020.09.21	Lagging inference networks and posterior collapse in variational autoencoders	Minjee Kim
2020.09.28	Probabilistic U-Net	Kyuri Kim
2020.09.28	RAFT: Recurrent All-Pairs Field Transforms for Optical Flow	Sungman Cho
2020.10.12	An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale	Sungman Cho
2020.10.12	Big Transfer (BiT): General Visual Representation Learning	Sungman Cho
2020.10.12	Regularizing Class-wise Predictions via Self-knowledge Distillation	Sungchul Kim
2020.10.19	Semi-Supervised StyleGAN for Disentanglement Learning	Minjee Kim
2020.10.26	Learning Visual Context by Comparison	Kyuri Kim
2020.11.02	Training Generative Adversarial Networks with Limited Data	Dain Eun
2020.11.02	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence	Sungchul Kim
2020.11.09	Representation Learing via Invariant Causal Mechanisms	Sungman Cho
2020.11.16	SEED: Self-supervised Distillation for Visual Representation	Sungman Cho
2020.11.23	Deep Clustering for Unsupervised Learning of Visual Features	Kyuri Kim
2020.11.30	Do Wide and Deep Networks Learn the Same Things? Uncovering How Neural Network Representations Vary with Width and Depth	Sungchul Kim
2021.01.08	Reliable Fidelity and Diversity Metrics for Generative Models	Minjee Kim
2021.01.15	Propagate Yourself: Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning	Sungman Cho
2021.01.22	Energy-based Generative Adversarial Network	Kyuri Kim
2021.01.22	Neural Bootstrapper	Kyuri Kim
2021.03.30	Domain Invariant Representation Learning with Domain Density Transformations	Ryoungwoo Jang
2021.11.10	UNETR: Transformers for 3D Medical Image Segmentation	Seungjun Lee
2021.11.10	TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up	Seungjoo Park
2021.11.24	Bidirectional Encoder Representations from Transformers	Inhwan Kim
2021.12.15	ConViT: Improving Vision Transformers with Soft Convolutional Inductive Biases	Hyunjung Kim
2021.12.15	GraphFPN: Graph Feature Pyramid Network for Object Detection	Junsik Kim
2022.01.05	Towards Real-World Blind Face Restoration with Generative Facial Prior	Sunggu Kyung
2022.01.05	Vision Transformers for Dense Prediction	Yujin Nam
2022.01.12	Florence: A New Foundation Model for Computer Vision	Kyungjin Cho
2022.01.12	FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping	Jiheon Jeong
2022.01.26	CoAtNet: Marrying Convolution and Attention for All Data Sizes	GyuJun Jeong
2022.01.26	Instant Neural Graphics Primitives with a Multiresolution Hash Encoding	Jooyoung Park

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