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

https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection

Deep Face Recognition: A Survey

https://github.com/Recognito-Vision/Linux-FaceRecognition-FaceLivenessDetection

DeepFakes and Beyond: A Survey of Face Manipulation and Fake Detection

https://github.com/SEU-WENJIA/ST-SepNet-Lightweight-LLMs-Meet-Adaptive-Hypergraphs

Deep Time Series Models: A Comprehensive Survey and Benchmark

https://github.com/SFFAI-AIKT/AIKT-Natural_Language_Processing

A Survey of Deep Learning Techniques for Neural Machine Translation

https://github.com/ShiqiYu/libfacedetection

Detect Faces Efficiently: A Survey and Evaluations

https://github.com/Sorananakiii/Fine-grained-image-recognition

Deep Learning for Fine-Grained Image Analysis: A Survey

https://github.com/TFG-Informatica-Enfermedad-Celiaca/Analisis-EC

Feature Selection: A Data Perspective

https://github.com/ThrupthiAnn/ThrupthiAnn.github.io

Explaining Deep Face Algorithms through Visualization: A Survey

https://github.com/TiagoCortinhal/SalsaNext

Deep Learning for 3D Point Clouds: A Survey

https://github.com/TornikeAm/Semantic-Segmentation-on-Pascal-VOC

A survey of loss functions for semantic segmentation

https://github.com/TsinghuaC3I/Efficient-Diffusion-Models

Efficient Diffusion Models: A Comprehensive Survey from Principles to Practices

https://github.com/UKPLab/beir

Pretrained Transformers for Text Ranking: BERT and Beyond

https://github.com/UW-AMO/TimeSeriesES-Cell

Generalized Kalman Smoothing: Modeling and Algorithms

https://github.com/VVRoseth/2.-Identifying_misinformation_in_disasters_FEMA

False Information on Web and Social Media: A Survey

https://github.com/YukiDaSlayer316/R-Tutorial

Structure Learning of Probabilistic Graphical Models: A Comprehensive Survey

https://github.com/ZHEvent/ZHEvent.github.io

What is Event Knowledge Graph: A Survey

https://github.com/aba450/Super-Resolution

A Deep Journey into Super-resolution: A survey

https://github.com/abhishekanand1710/noiseandbias

Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application

https://github.com/adioshun/gitBook_DeepSlam

Keyframe-based monocular SLAM: design, survey, and future directions

https://github.com/adverml/advopenood

Generalized Out-of-Distribution Detection: A Survey

https://github.com/agoodweathercc/EmbeddingEval

Graph Embedding Techniques, Applications, and Performance: A Survey

https://github.com/ai4eic/eic-rag-project

Retrieval-Augmented Generation for Large Language Models: A Survey

https://github.com/aimagelab/LLaVA-MORE

The Revolution of Multimodal Large Language Models: A Survey

https://github.com/ait-aecid/anomaly-detection-log-datasets

Deep Learning for Anomaly Detection in Log Data: A Survey

https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection

Deep Face Recognition: A Survey

https://github.com/amanshenoy/pose-invariant-face-recognition

A Comprehensive Survey on Pose-Invariant Face Recognition

https://github.com/anitagraser/movement-analysis-tools

A Survey on Spatio-temporal Data Analytics Systems

https://github.com/anshu123priya/Real-v-s-Fake-Emotion-Challenge

Deep Facial Expression Recognition: A Survey

https://github.com/anshu123priya/Real-vs-Fake-Emotion-Challenge

Deep Facial Expression Recognition: A Survey

https://github.com/antoinedemathelin/openood

Generalized Out-of-Distribution Detection: A Survey

https://github.com/arbazgormani/HR_Analytics_Data_Scientists

An Extensive Analytical Approach on Human Resources using Random Forest Algorithm

https://github.com/asjad99/datascience-GYM

Challenges in Deploying Machine Learning: a Survey of Case Studies

https://github.com/astrolabsoftware/fink-science

Paying Attention to Astronomical Transients: Introducing the Time-series Transformer for Photometric Classification

https://github.com/avi-jit/SWOW-eval

A Survey of Word Embeddings Evaluation Methods

https://github.com/avinashbarnwal/Deep-Learning

Deep Neural Networks and Tabular Data: A Survey

https://github.com/awe2/GZ2_project

Deep Learning at Scale for the Construction of Galaxy Catalogs in the Dark Energy Survey

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