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regression

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

6

보존된 평가 기록

15

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/JusperLee/UtterancePIT-Speech-Separation

Permutation Invariant Training of Deep Models for Speaker-Independent Multi-talker Speech Separation

https://github.com/KARAASLAN-AI/re3-object-tracking

Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects

https://github.com/KaiQiao1992/ETECRM

Effective and efficient ROI-wise visual encoding using an end-to-end CNN regression model and selective optimization

https://github.com/KamalM8/Few-Shot-learning-Fashion

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/KangJier/polarMask

PolarMask: Single Shot Instance Segmentation with Polar Representation

https://github.com/Kasumigaoka-Utaha/Pytorch-implementation-of-DeepRegressionForests

Deep Regression Forests for Age Estimation

https://github.com/KatyNTsachi/Hierarchical-RL

Implicit Quantile Networks for Distributional Reinforcement Learning

https://github.com/Kchu/DeepRL_CK

Distributional Reinforcement Learning with Quantile Regression

https://github.com/Kelechukwu1/PidginUNMT

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

https://github.com/Kyushik/Predictive-Uncertainty-Estimation-using-Deep-Ensemble

Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles

https://github.com/LKLQQ/LEO

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/LMikeH/ocbnn-lmikh

Weight Uncertainty in Neural Networks

https://github.com/LONG-9621/IQA_02

Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank

https://github.com/LinRiver/YOLOv3-on-LISA-Traffic-Sign-Detection-with-darknet

Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression

https://github.com/LlucPuigCodina/Simultaneous-Mean-Variance-Regression

Simultaneous Mean-Variance Regression

https://github.com/Lukeasargen/targetClassifier

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/MCKnaus/dmlmt

High-Dimensional Metrics in R

https://github.com/MIC-DKFZ/TractSeg

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/MIT-Omnipush/omnipush-metalearning-baselines

Attentive Neural Processes

https://github.com/Manchery/DeepPose

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/MartinSpindler/hdm

High-Dimensional Metrics in R

https://github.com/MateusMaiaDS/rmachines

Random Machines: A bagged-weighted support vector model with free kernel choice

https://github.com/MattyChoi/PoseMachines

DeepPose: Human Pose Estimation via Deep Neural Networks

https://github.com/MaximeVandegar/Papers-in-100-Lines-of-Code/tree/main/Model_Agnostic_Meta_Learning_for_Fast_Adaptation_of_Deep_Networks

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Medabid1/Bayes-by-backprob

Weight Uncertainty in Neural Networks

https://github.com/MehdiChelh/Zoo

Weight Uncertainty in Neural Networks

https://github.com/Microsoft/fairlearn

Fair Regression: Quantitative Definitions and Reduction-based Algorithms

https://github.com/Mind23-2/MindCode-1

Side-Aware Boundary Localization for More Precise Object Detection

https://github.com/Mind23-2/MindCode-101/tree/main/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Mind23-2/MindCode-152

Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning

https://github.com/Mind23-2/MindCode-173

SPP-Net: Deep Absolute Pose Regression with Synthetic Views

https://github.com/Mind23-2/MindCode-177

Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression

https://github.com/Mind23-2/MindCode-3/tree/main/MAML

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/Mind23-2/MindCode-3/tree/main/MIMO-UNet

Probabilistic MIMO U-Net: Efficient and Accurate Uncertainty Estimation for Pixel-wise Regression

https://github.com/Mind23-2/MindCode-55

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/MindCode-4/code-11/tree/main/AccSGD-Parallelizing-Stochastic-Gradient-Descent

Parallelizing Stochastic Gradient Descent for Least Squares Regression: mini-batching, averaging, and model misspecification

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.