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MI2RL Paper Study

Contents

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