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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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보존된 평가 기록

5

코드

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

https://github.com/RobinBaumann/TF-AtlasNetV2

Learning elementary structures for 3D shape generation and matching

https://github.com/RobustNLP/TestTranslation

Structure-Invariant Testing for Machine Translation

https://github.com/Rongpeng-Lin/StarGAN-Multi-domain-use-tensorflow

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

https://github.com/Rudedaisy/attention-is-all-you-need-pytorch

Attention Is All You Need

https://github.com/Russzheng/CS280

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

https://github.com/Rust401/pixel2piexl

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/S-Abdelnabi/awt

Regularizing and Optimizing LSTM Language Models

https://github.com/S-aiueo32/dni-demo

Deep Network Interpolation for Continuous Imagery Effect Transition

https://github.com/SCismycat/bert_code_view

Attention Is All You Need

https://github.com/SFFAI-AIKT/AIKT-Natural_Language_Processing

A Survey on Document-level Neural Machine Translation: Methods and Evaluation

https://github.com/SHAHBAZSIDDIQI/DataScience

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

https://github.com/STVIR/pysot

SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks

https://github.com/SUPERSHOPxyz/stylegan3-gradient

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/SYangDong/bert-with-frozen-code

Attention Is All You Need

https://github.com/SachinIchake/KALM

Regularizing and Optimizing LSTM Language Models

https://github.com/SamuelCahyawijaya/indobenchmark-toolkit

IndoNLG: Benchmark and Resources for Evaluating Indonesian Natural Language Generation

https://github.com/Sang2710/Bringing-old-photos-back-to-life

Bringing Old Photos Back to Life

https://github.com/Sangarshanan/Exploring-Language-similarities

Exploring Language Similarities with Dimensionality Reduction Technique

https://github.com/SarderLab/WSI-cycleGAN

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

https://github.com/Satan012/BERT

Attention Is All You Need

https://github.com/SathwikTejaswi/Neural-Image-Captioning

Show and Tell: A Neural Image Caption Generator

https://github.com/SaubanMusaddiq/Paraphrase_Detection_on_PAWS

PAWS: Paraphrase Adversaries from Word Scrambling

https://github.com/ScientiaEtVeritas/NeuralMachineTranslation

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/SdahlSean/RangerOptimizerTensorflow

Lookahead Optimizer: k steps forward, 1 step back

https://github.com/SeniorDev009/ONNX-project

Unsupervised Image-to-Image Translation Networks

https://github.com/SenthilCaesar/CNN-Brain-MRI-Segmentation

QuickNAT: A Fully Convolutional Network for Quick and Accurate Segmentation of Neuroanatomy

https://github.com/SeonbeomKim/Python-Bype_Pair_Encoding

Neural Machine Translation of Rare Words with Subword Units

https://github.com/SeonbeomKim/Python-Byte_Pair_Encoding

Neural Machine Translation of Rare Words with Subword Units

https://github.com/SeonbeomKim/TensorFlow-pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/SeonghoBaek/CycleGAN

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

https://github.com/SeoroMin/transformer_pytorch

Attention Is All You Need

https://github.com/SeoroMin/transformer_pytorch_ver2

Attention Is All You Need

https://github.com/SergiBragos/DLAI-2021-project

Improving Non-Intrusive Load Disaggregation through an Attention-Based Deep Neural Network

https://github.com/SharifAmit/VTGAN

Attention2AngioGAN: Synthesizing Fluorescein Angiography from Retinal Fundus Images using Generative Adversarial Networks

https://github.com/SharifElfouly/easy-model-zoo

YOLACT: Real-time Instance Segmentation

https://github.com/ShashwatNigam99/MARBLER

Emergence of Grounded Compositional Language in Multi-Agent Populations

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