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코드

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

https://github.com/Jayanthipalanichamy/ElucidateTheScene

Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge

https://github.com/JeffreyJosanne/nematus_tf

Using the Output Embedding to Improve Language Models

https://github.com/JeheonPark596/sentimentsurvival

Attention Is All You Need

https://github.com/JessikaSmith/language_model

Regularizing and Optimizing LSTM Language Models

https://github.com/JiachenMao/TransfornerPrune

Tensor2Tensor for Neural Machine Translation

https://github.com/JiawangBian/sc_depth_pl

Auto-Rectify Network for Unsupervised Indoor Depth Estimation

https://github.com/Jittor/InstanceSegmentation-jittor

YOLACT: Real-time Instance Segmentation

https://github.com/JobQiu/hackrice

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/JoegameZhou/STGAN

STGAN: A Unified Selective Transfer Network for Arbitrary Image Attribute Editing

https://github.com/JohanYe/CycleGAN-Pokemon

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/JohanZYe/CycleGAN-Pokemon

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/JohannLee1996/bert

Attention Is All You Need

https://github.com/JonathanLehner/TecoGan-downloader

Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation

https://github.com/JonathanRaiman/tensorflow_qrnn

Quasi-Recurrent Neural Networks

https://github.com/JosephineRabbit/Relation-GAN

When Relation Networks meet GANs: Relation GANs with Triplet Loss

https://github.com/Joshuaoneheart/Fintech-Final-Report

Attention Is All You Need

https://github.com/JuliaWolleb/diffusion-anomaly

Diffusion Models for Medical Anomaly Detection

https://github.com/JungSeokWoo/Lightweight-LaneDetection

Towards Lightweight Lane Detection by Optimizing Spatial Embedding

https://github.com/JunnYu/x-transformers-paddle

Transformers without Tears: Improving the Normalization of Self-Attention

https://github.com/JunnYu/xlm_paddle

Cross-lingual Language Model Pretraining

https://github.com/Justin-Tan/generative-compression

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/KHKHG/yolact

YOLACT: Real-time Instance Segmentation

https://github.com/KaiyuanZh/censor

Sparse Communication for Distributed Gradient Descent

https://github.com/KakaoBrain/torchgpipe

GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism

https://github.com/Kakoedlinnoeslovo/fairseq

Convolutional Sequence to Sequence Learning

https://github.com/Kal213/StarGAN-Tutorial-Tensorflow-2.3

StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation

https://github.com/Katsumata420/bert_mlm

Attention Is All You Need

https://github.com/KbeautyHair/BaselineModel

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/KeithGalli/pycon2020

Attention Is All You Need

https://github.com/Kelechukwu1/PidginUNMT

Loss in Translation: Learning Bilingual Word Mapping with a Retrieval Criterion

https://github.com/Kevin-Vora/bert-embedding-gluonnlp-edit-

Attention Is All You Need

https://github.com/KevinJia1212/yolact_cityscapes_550

YOLACT: Real-time Instance Segmentation

https://github.com/KevinYoung98/Seq2Seq-PyTorch

Effective Approaches to Attention-based Neural Machine Translation

https://github.com/Khalid-Jamal/annotated-transformer-Arabic

Attention Is All You Need

https://github.com/Kirankumaraswamy/Transformers

Attention Is All You Need

https://github.com/Kitsunetic/Optimizer-Visualization

On the Variance of the Adaptive Learning Rate and Beyond

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