https://github.com/leaderj1001/RandWireNN
Exploring Randomly Wired Neural Networks for Image Recognition
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/leondgarse/Keras_efficientnet_v2
https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/mobilenetv3_family
FBNetV3: Joint Architecture-Recipe Search using Predictor Pretraining
https://github.com/lgerrets/rl18-curiosity
https://github.com/liamcli/darts
https://github.com/liamcli/randomNAS_release
Random Search and Reproducibility for Neural Architecture Search
https://github.com/libffcv/ffcv-imagenet
Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints
https://github.com/lightaime/sgas
https://github.com/linhduongtuan/Fruits_Vegetables_Classifier_WebApp
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lippman1125/darts_notes
https://github.com/liuyuezhang/pyrl
https://github.com/liuyugeng/baadd
https://github.com/liyiyuian/Deep-Learning
https://github.com/liyy201912/PaS
https://github.com/llSourcell/OpenAI_Five_vs_Dota2_Explained
https://github.com/lmb-freiburg/autodispnet
https://github.com/lordzth666/AutoShrink
AutoShrink: A Topology-aware NAS for Discovering Efficient Neural Architecture
https://github.com/lorylei/DARTS-et
https://github.com/lpirola13/flower-recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lpirola13/flower_recognizer
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lpxtt/simtrack
GLiT: Neural Architecture Search for Global and Local Image Transformer
https://github.com/lucifer443/SpineNet-Pytorch
SpineNet: Learning Scale-Permuted Backbone for Recognition and Localization
https://github.com/lucylow/Covid_Control
https://github.com/lukemelas/EfficientNet-PyTorch
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lukesin/nas-for-kws-2
ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardware
https://github.com/luuchung/cifar-100
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lvweiwolf/efficientdet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/lyqcom/efficientnet
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/machinelearning-goettingen/EfficientNet-ModelScaling
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/maragori/DeepfakeForensics-v1
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marcointrovigne/WeatherDetection
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/marijnvk/LargeScaleEvolution
https://github.com/mark-gluzman/NmodelPPO
https://github.com/marload/DeepRL-TensorFlow2
https://github.com/maxwelltsai/DeepGalaxy
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/mayankk6196/darts-python
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.