Papers with Code 아카이브
Neural Architecture Search
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 1
- 평가표
- 26
- 보존된 평가 기록
- 424
Neural architecture search (NAS) is a technique for automating the design of artificial neural networks (ANN), a widely used model in the field of machine learning. NAS essentially takes the process of a human manually tweaking a neural network and learning what works well, and automates this task to discover more complex architectures.
Image Credit : NAS with Reinforcement Learning
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/xuanhungho/nsga
Regularized Evolution for Image Classifier Architecture Search
https://github.com/xufanxiong/classification-of-caltech-256
Learning Transferable Architectures for Scalable Image Recognition
https://github.com/xwu6614555/MobileNetV3-Mxnet
https://github.com/xxlya/COS598D_Assignment1
https://github.com/yakhyo/head-pose-estimation
https://github.com/yan-roo/SpineNet-Pytorch
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/yangsenius/Pose-Neural-Fabrics-Search
https://github.com/yangyucheng000/Paper-2/tree/main/SharpDRO-ms-main
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
https://github.com/yangyucheng000/ViPNAS
ViPNAS: Efficient Video Pose Estimation via Neural Architecture Search
https://github.com/yangyucheng000/mnasnet/blob/main/mnasnet.py
MnasNet: Platform-Aware Neural Architecture Search for Mobile
https://github.com/yashkant/ENAS-Quantized-Neural-Networks
https://github.com/yashkant/PNAS-Binarized-Neural-Networks
https://github.com/yawen-d/transnasbench
https://github.com/yoavalon/Quadcopter-env
https://github.com/yoavalon/QuadcopterReinforcementLearning
https://github.com/youngwoo-yoon/Practical-tips-for-lightweight-deep-learning
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/yuke93/ReconfigISP
ReconfigISP: Reconfigurable Camera Image Processing Pipeline
https://github.com/yuz1wan/video_distillation
https://github.com/zbyte64/pytorch-dagsearch
https://github.com/zh320/realtime-semantic-segmentation-pytorch
Rethinking Dilated Convolution for Real-time Semantic Segmentation
https://github.com/zhangximing666/Auto-DeepLab_MS
Auto-DeepLab: Hierarchical Neural Architecture Search for Semantic Image Segmentation
https://github.com/zhaoyuesun/phee
Automated Concatenation of Embeddings for Structured Prediction
https://github.com/zhengjian2322/net2net
Simple And Efficient Architecture Search for Convolutional Neural Networks
https://github.com/zhoudaquan/AutoSpace
AutoSpace: Neural Architecture Search with Less Human Interference
https://github.com/zitonghuangcynthia/SWAT-NN
SWAT-NN: Simultaneous Weights and Architecture Training for Neural Networks in a Latent Space
https://github.com/zjlab-ammi/llm4rl
https://github.com/zsef123/EfficientNets-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/zsz-hst/RL_single_chase_point
https://github.com/zym1119/Partial-Order-Pruning-Demo
Partial Order Pruning: for Best Speed/Accuracy Trade-off in Neural Architecture Search
https://gitlab.com/birder/birder
MnasNet: Platform-Aware Neural Architecture Search for Mobile
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.