Papers with Code 아카이브

3D Face Modelling

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코드

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

https://github.com/jongornet14/HyperController

Proximal Policy Optimization Algorithms

https://github.com/joonson/yousaidthat

You said that?

https://github.com/joovvhan/ecapa-tdnn

ArcFace: Additive Angular Margin Loss for Deep Face Recognition

https://github.com/jooyae/NVIDIA_STYLEGAN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/jooyounghun/AI-Team-5

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/jordan-bird/synthetic-fruit-image-generator

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/jqueeney/geppo

Generalized Policy Improvement Algorithms with Theoretically Supported Sample Reuse

https://github.com/jrobine/actor-critic

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

https://github.com/jsztompka/MultiAgent-PPO

Proximal Policy Optimization Algorithms

https://github.com/jsztompka/PPO-demo

Proximal Policy Optimization Algorithms

https://github.com/jtcass01/SIMON

Deep Residual Learning for Image Recognition

https://github.com/jtchen0528/PCL-I2G

Learning Self-Consistency for Deepfake Detection

https://github.com/jtiger958/pytorch-computer-vision-basic

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/jtiger958/pytorch-computer-vision-tutorial

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/jtpils/treegan

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/jturner65/Getup-DartEnv

Multi-Goal Reinforcement Learning: Challenging Robotics Environments and Request for Research

https://github.com/juergenlandauer/Maya-Challenge

Focal Loss for Dense Object Detection

https://github.com/julik43/IIMAS-USCS

Deep Residual Learning for Image Recognition

https://github.com/juliusfrost/Research-Paper-Implementations

Proximal Policy Optimization Algorithms

https://github.com/juliusfrost/dreamer-pytorch

Dream to Control: Learning Behaviors by Latent Imagination

https://github.com/juliuskunze/cwvae-jax

Learning Latent Dynamics for Planning from Pixels

https://github.com/junebeomstics/tff

Self-Supervised Transformers for fMRI representation

https://github.com/junming-yang/mopo

MOPO: Model-based Offline Policy Optimization

https://github.com/justinbellucci/cnn-visualizations-pytorch

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/justinjfu/diagnosing_qlearning

Diagnosing Bottlenecks in Deep Q-learning Algorithms

https://github.com/justinjohn0306/EverybodyDanceNow-Colab

Everybody Dance Now

https://github.com/justinshenk/fer

Challenges in Representation Learning: A report on three machine learning contests

https://github.com/justusschock/shape-constrained-network

Super-realtime facial landmark detection and shape fitting by deep regression of shape model parameters

https://github.com/jvidals09/Decentralized-and-multi-agent-control-of-Franka-Emika-Panda-robot-in-continuous-task-execution

Proximal Policy Optimization Algorithms

https://github.com/jvoynow/ConvNet-Architectures

Deep Residual Learning for Image Recognition

https://github.com/jw1401/PPO-Tensorflow-2.0

Proximal Policy Optimization Algorithms

https://github.com/jyqhahah/rl_maddpg_matd3

Addressing Function Approximation Error in Actor-Critic Methods

https://github.com/k-miran/hear

Deep Residual Learning for Image Recognition

https://github.com/kabrau/FaceDetection

WIDER FACE: A Face Detection Benchmark

https://github.com/kagami/go-face

FaceNet: A Unified Embedding for Face Recognition and Clustering

https://github.com/kaichengyan/facenet-darknet-pytorch

FaceNet: A Unified Embedding for Face Recognition and Clustering

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