Papers with Code 아카이브

3D Face Modelling

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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하위 태스크

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평가표

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보존된 평가 기록

3

코드

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

https://github.com/Anuranjan-Kumar/kaggle_cats-vs-dogs

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/AppliedDataSciencePartners/WorldModels

World Models

https://github.com/ArMaxik/Progressive_growing_of_GANs-Pytorch_implementation

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/Aravind-11/AI-Gaming

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/IITM_Saastra

Proximal Policy Optimization Algorithms

https://github.com/Aravind-11/Multi-Agent-RL

Proximal Policy Optimization Algorithms

https://github.com/ArifRaihan/Deeper-CNN-Architectures-for-Image-Classification

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Armxyz1/Results-on-RFW

SphereFace: Deep Hypersphere Embedding for Face Recognition

https://github.com/Arneli/image-generator-for-BScThesis

Empirically Analyzing the Effect of Dataset Biases on Deep Face Recognition Systems

https://github.com/Arpan-Mishra/Selfie-Filter-Project

Facial Key Points Detection using Deep Convolutional Neural Network - NaimishNet

https://github.com/ArsenalCheng/Meta-Adapter

Learning to Prompt for Vision-Language Models

https://github.com/ArturPrzybysz/MNIST-siamese

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/ArturPrzybysz/siamese-CNN-cat-vs-dogs

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Aruie/Aru_StyleGAN

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/Aryan05/Generative-Modelling-of-Images-from-Speech_Speech2Face

Wav2Pix: Speech-conditioned Face Generation using Generative Adversarial Networks

https://github.com/Asap7772/understanding-rlhf

Mastering Visual Continuous Control: Improved Data-Augmented Reinforcement Learning

https://github.com/Ash-Lee233/PGAN

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/Ashigarg123/ShiftySpeech

Automatic speaker verification spoofing and deepfake detection using wav2vec 2.0 and data augmentation

https://github.com/AshikaAnand12/FaceManipulation

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

https://github.com/Ashish013/FaceNet_TF-2.0

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Asthestarsfalll/LightCNN-MegEngine

A Light CNN for Deep Face Representation with Noisy Labels

https://github.com/AswillHatter/Sondi1

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/AswillHatter/Union

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/Atcold/torch-TripletEmbedding

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/AtlasCoCo/Darknet

Focal Loss for Dense Object Detection

https://github.com/AurelianTactics/bcq_tensorflow

Off-Policy Deep Reinforcement Learning without Exploration

https://github.com/Avditvs/proGAN

Progressive Growing of GANs for Improved Quality, Stability, and Variation

https://github.com/AveryGriffin/NarutoCV

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/Ayoob7/facenet-face-recognition

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/AyushAniket/ResNET-and-various-normalizations

Deep Residual Learning for Image Recognition

https://github.com/B-C-WANG/ReinforcementLearningInAutoPilot

World Models

https://github.com/BFAnas/BranchingDQN_keras

Action Branching Architectures for Deep Reinforcement Learning

https://github.com/BFD91/facial-beauty-rating-tinder-bot

SCUT-FBP5500: A Diverse Benchmark Dataset for Multi-Paradigm Facial Beauty Prediction

https://github.com/BIG-CHENG/FaceRec

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/PPOAgent.py

Proximal Policy Optimization Algorithms

https://github.com/BIT-aerial-robotics/AquaML/blob/2.1.11/AquaML/rlalgo/TD3Agent.py

Addressing Function Approximation Error in Actor-Critic Methods

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