Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

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평가표

2

보존된 평가 기록

3

코드

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

https://github.com/miroblog/tf_deep_rl_trader

Proximal Policy Optimization Algorithms

https://github.com/mit-realm/neuriss

Proximal Policy Optimization Algorithms

https://github.com/mjhucla/TF-mRNN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mjhucla/mRNN-CR

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mjoannou/audiovisual-moments-in-time

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mklissa/dceo

Towards Better Laplacian Representation in Reinforcement Learning with Generalized Graph Drawing

https://github.com/mkocabas/focal-loss-keras

Focal Loss for Dense Object Detection

https://github.com/mkonuk66/EfficientFER

https://github.com/mktoid/made-thousand-facial-landmarks

Wing Loss for Robust Facial Landmark Localisation with Convolutional Neural Networks

https://github.com/mlaskowski17/Pulse

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

https://github.com/mlpc-ucsd/PRTR

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/mlpocprojects/facenet-poc

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/mlu355/MetadataNorm

Representation Learning with Statistical Independence to Mitigate Bias

https://github.com/mlvccn/bmtc_transferattackvid

Deep Residual Learning for Image Recognition

https://github.com/mlvlab/dapt

Learning to Prompt for Vision-Language Models

https://github.com/mminamina/Transfer_Learning_NaimishNet---Keypoints_Detection

Deep Residual Learning for Image Recognition

https://github.com/mminamina/Transfer_Learning_ResNet18---Keypoints_Detection

Deep Residual Learning for Image Recognition

https://github.com/mnguyen0226/image-augmentation-dnn-performance

Deep Residual Learning for Image Recognition

https://github.com/mnikitin/Learn-Convolutional-Neural-Network-for-Face-Anti-Spoofing

Learn Convolutional Neural Network for Face Anti-Spoofing

https://github.com/moado/expression_recognition

Real-time Convolutional Neural Networks for Emotion and Gender Classification

https://github.com/moallafatma/Breast_Cancer_Detection_Classification

Focal Loss for Dense Object Detection

https://github.com/mobilesec/arcface-tensorflowlite

Deep Residual Learning for Image Recognition

https://github.com/moddy2024/resnet-9

Deep Residual Learning for Image Recognition

https://github.com/modelhub-ai/arc-face

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/modelhub-ai/emotion-fer-plus

Training Deep Networks for Facial Expression Recognition with Crowd-Sourced Label Distribution

https://github.com/modelhub-ai/network-in-network

Network In Network

https://github.com/modelhub-ai/vgg-19

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/modestyachts/ImageNetV2

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mohaEs/Train-Predict-Landmarks-by-flat-net

Automatic vocal tract landmark localization from midsagittal MRI data

https://github.com/mohamedameen93/German-Traffic-Sign-Classification-Using-TensorFlow

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/mohammadimathstar/grlgq

Generalized Relevance Learning Grassmann Quantization

https://github.com/mohanliu/HRnet-semantic-segmentation-exp

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/mohitwildbeast/Facial-Recognition-Using-FaceNet-Siamese-One-Shot-Learning

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/monoclecat/latent-conditioned-SAC

Discovering Diverse Solutions in Deep Reinforcement Learning by Maximizing State-Action-Based Mutual Information

https://github.com/montaserFath/Reinforcement-Learning-for-Prosthetics

Continuous control with deep reinforcement learning

https://github.com/monte-ai/expert-augmented-acktr

Scalable trust-region method for deep reinforcement learning using Kronecker-factored approximation

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