https://github.com/Recognito-Vision/Android-FaceRecognition-FaceLivenessDetection
Searching Central Difference Convolutional Networks for Face Anti-Spoofing
Papers with Code 아카이브
AutoML
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 3
- 평가표
- 4
- 보존된 평가 기록
- 11
Automated Machine Learning (AutoML) is a general concept which covers diverse techniques for automated model learning including automatic data preprocessing, architecture search, and model selection. Source: Evaluating recommender systems for AI-driven data science (1905.09205)
Source: CHOPT : Automated Hyperparameter Optimization Framework for Cloud-Based Machine Learning Platforms
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/RhythmSyed/NAS_PerformanceEstimation
Generalized Latency Performance Estimation for Once-For-All Neural Architecture Search
https://github.com/RualPerez/AutoML
https://github.com/S4iz/beta
https://github.com/SAGNIKMJR/CODEBRIM_MetaQNN
https://github.com/SLDGroup/NN_Mass
https://github.com/SPark9625/PyTorch-Proximal-Policy-Optimization
https://github.com/SaharR1372/DM_Style_matching
https://github.com/Sakib1263/MobileNet-1D-2D-Tensorflow-Keras
https://github.com/SalvatoreCognetta/reasoning-agent-project
https://github.com/SharifElfouly/easy-model-zoo
https://github.com/Shmuelnaaman/Fast_Lightweight_Hyperparameter-Optimization-
https://github.com/ShunLu91/DU-DARTS
https://github.com/ShunLu91/Single-Path-One-Shot-NAS
Single Path One-Shot Neural Architecture Search with Uniform Sampling
https://github.com/SifatMd/Research-Papers
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Somya-Patra/Shell_ai
https://github.com/SourangshuGhosh/CGP-CNN-Design
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/SpikeKing/mobilenet_v3
https://github.com/StevRamos/video_summarization
Supervised Video Summarization via Multiple Feature Sets with Parallel Attention
https://github.com/StevenGerrad/large-scale-Evolution-Net
https://github.com/Stippler/cow-simulator
https://github.com/SunDoge/efficientnet-pytorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/SuphanutN/Thai-NER-BiLSTM-WordCharEmbedding
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/SuphanutN/Thai-NER-BiLSTMCRF-WordCharEmbedding
Optimal Hyperparameters for Deep LSTM-Networks for Sequence Labeling Tasks
https://github.com/SweetyTian/efficientdet
https://github.com/Syavaprd/mobilenet_v3
https://github.com/Sycor4x/lipo
https://github.com/TAMU-VITA/GAN-Slimming
AutoGAN-Distiller: Searching to Compress Generative Adversarial Networks
https://github.com/TabishA/cgpunet
A Genetic Programming Approach to Designing Convolutional Neural Network Architectures
https://github.com/Tarzanagh/FedNest
Optimizing Millions of Hyperparameters by Implicit Differentiation
https://github.com/TezRomacH/layer-to-layer-pytorch
Training Large Neural Networks with Constant Memory using a New Execution Algorithm
https://github.com/TheConstant3/MobileNetV3-Keras
https://github.com/Tingelam/tianchi_gd_defect_pytorch_round1
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/Tirth27/Skin-Cancer-Classification-using-Deep-Learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/Tobias-K93/media-bias-prediction
https://github.com/TranNhiem/Neural_Network_Architecture_Search
$μ$NAS: Constrained Neural Architecture Search for Microcontrollers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.