Papers with Code 아카이브
Structured Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 1
Structured Prediction is an area of machine learning focusing on representations of spaces with combinatorial structure, and algorithms for inference and parameter estimation over these structures. Core methods include both tractable exact approaches like dynamic programming and spanning tree algorithms as well as heuristic techniques such as linear programming relaxations and greedy search.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/delirecs/facial-keypoints-heatmaps
https://github.com/digital-thinking/deep-posemachine
https://github.com/eth-ait/spl
https://github.com/evangravelle/AI-projects
https://github.com/fcr3/OpenVINO_Tutorials
https://github.com/fcr3/wine_detector
https://github.com/fengyidong123/openpose-master
https://github.com/fhalamos/semantic-segmentation-with-cnn
https://github.com/gidariss/DRR_struct_pred
Detect, Replace, Refine: Deep Structured Prediction For Pixel Wise Labeling
https://github.com/gorjanradevski/vsepp_tensorflow
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/harshalsonioo1/hpe
https://github.com/hazimehh/hierScale
Learning Hierarchical Interactions at Scale: A Convex Optimization Approach
https://github.com/iinemo/lm-polygraph
Uncertainty Estimation in Autoregressive Structured Prediction
https://github.com/janblumenkamp/esa-kelvin-pose-estimation
https://github.com/jarrodanderson/openpose-demo
https://github.com/john-hewitt/conditional-probing
Fast and Accurate Entity Recognition with Iterated Dilated Convolutions
https://github.com/jreisam/Unity-OpenPose-Edutable
https://github.com/juliakreutzer/bandit-cdec
https://github.com/juliakreutzer/bandit-neuralmonkey
Bandit Structured Prediction for Neural Sequence-to-Sequence Learning
https://github.com/kadarakos/mulisera
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/kampta/pytorch-distributions
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
https://github.com/kjasinska/ltls
https://github.com/laobaiswag/openpose1
https://github.com/layumi/Person_reID_baseline_pytorch
Deep Metric Learning via Lifted Structured Feature Embedding
https://github.com/ldmt-muri/alignment-with-openfst
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
https://github.com/leequant761/Gumbel-SSVAE
The Concrete Distribution: A Continuous Relaxation of Discrete Random Variables
https://github.com/leggedrobotics/raw_image_pipeline
https://github.com/leolee99/CLIP_ITM
VSE++: Improving Visual-Semantic Embeddings with Hard Negatives
https://github.com/liang-faan/openpose
https://github.com/lifu-tu/BenchmarkingApproximateInference
Benchmarking Approximate Inference Methods for Neural Structured Prediction
https://github.com/lifu-tu/ENGINE
Learning Approximate Inference Networks for Structured Prediction
https://github.com/lifu-tu/INFNET
Learning Approximate Inference Networks for Structured Prediction
https://github.com/limi44/Parkinson-s-Pose-Estimation-Dataset
https://github.com/lit-leo/inner-convex-nn
https://github.com/lncarter/Openpose
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.