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regression
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코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/freemeson/multinomial
https://github.com/frgfm/Holocron
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
https://github.com/fyangneil/DSFPN
Dually Supervised Feature Pyramid for Object Detection and Segmentation
https://github.com/gabriel-sgama/semantic-superpoint
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/galdeia/feat
Learning concise representations for regression by evolving networks of trees
https://github.com/gaspardbe/guess_and_check
Adaptive Concentration of Regression Trees, with Application to Random Forests
https://github.com/gatheluck/pytorch-selectivenet
SelectiveNet: A Deep Neural Network with an Integrated Reject Option
https://github.com/gau-nernst/CenterNet
Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
https://github.com/gauenk/faiss_fork
Link and code: Fast indexing with graphs and compact regression codes
https://github.com/gbernstein6/private_bayesian_regression
https://github.com/gcucurull/maml_flax
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/geaxgx/depthai_blazepose
https://github.com/gebob19/REPTILE-Metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/cscd94-metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/gebob19/cscd94_metalearning
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/georghess/pmb-nll
Estimating and Evaluating Regression Predictive Uncertainty in Deep Object Detectors
https://github.com/ghimiredhikura/Complex-YOLO-V3
https://github.com/ghimiredhikura/Complex-YOLOv3
https://github.com/gift-surg/fetal_brain_seg
https://github.com/github-jnauta/pytorch-pne
Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles
https://github.com/glarbi/Face-detection
Selective Refinement Network for High Performance Face Detection
https://github.com/godcrampy/re3-custom
Re3 : Real-Time Recurrent Regression Networks for Visual Tracking of Generic Objects
https://github.com/goepp/graphseg
https://github.com/goldblum/AdversarialQuerying
https://github.com/gonepas/stock-predict
Stock Price Prediction Using Machine Learning and LSTM-Based Deep Learning Models
https://github.com/google-deepmind/neural-processes
https://github.com/google-research/google-research/tree/master/neural_additive_models
Neural Additive Models: Interpretable Machine Learning with Neural Nets
https://github.com/google/trax
Advantage-Weighted Regression: Simple and Scalable Off-Policy Reinforcement Learning
https://github.com/gregoritoo/ABCDflow
Structure Discovery in Nonparametric Regression through Compositional Kernel Search
https://github.com/gregtozzi/deep_learning_celnav
https://github.com/grifon-239/diploma
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression
https://github.com/grtzsohalf/SpeechNet-codebase
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/gtegner/hyper-gan
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning
https://github.com/guluguluhhhh/contrib/tree/37-2/application/multi-task-learning-using-uncertainty-to
Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics
https://github.com/guoyongcs/ews
Closed-loop Matters: Dual Regression Networks for Single Image Super-Resolution
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.