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태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

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코드

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

https://github.com/mergarsal/FastCertRelPose

TEASER: Fast and Certifiable Point Cloud Registration

https://github.com/mgiurca/UnsupervisedMultimodalCD

Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

https://github.com/mgiurca/unsupervised-multimodal-cd

Deep Image Translation with an Affinity-Based Change Prior for Unsupervised Multimodal Change Detection

https://github.com/mhamedLmarbouh/Attention-is-all-you-need

Attention Is All You Need

https://github.com/mhardalov/customer-support-chatbot

Towards Automated Customer Support

https://github.com/mhaseeb123/DeepMSim

Attention Is All You Need

https://github.com/mhjabreel/CharCnn_Keras

An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling

https://github.com/mhw32/multimodal-vae-public

Multimodal Generative Models for Scalable Weakly-Supervised Learning

https://github.com/mia-workshop/mia-shared-task-2022

MKQA: A Linguistically Diverse Benchmark for Multilingual Open Domain Question Answering

https://github.com/michael-wzhu/mpnet_zh

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/michalgeyer/pnp-diffusers

Plug-and-Play Diffusion Features for Text-Driven Image-to-Image Translation

https://github.com/mickan123/CycleGAN

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

https://github.com/microcoder-py/attn-is-all-you-need

Attention Is All You Need

https://github.com/microsoft/BlingFire

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/microsoft/CoCosNet

Cross-domain Correspondence Learning for Exemplar-based Image Translation

https://github.com/microsoft/MPNet

MASS: Masked Sequence to Sequence Pre-training for Language Generation

https://github.com/microsoft/MT-DNN

On the Variance of the Adaptive Learning Rate and Beyond

https://github.com/microsoft/Table-Pretraining

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/microsoft/deepnmt

Very Deep Transformers for Neural Machine Translation

https://github.com/microsoft/fastseq

BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension

https://github.com/microsoft/onnxruntime-training-examples

Attention Is All You Need

https://github.com/microsoft/protein-sequence-models

Neural Machine Translation in Linear Time

https://github.com/microsoft/recommenders

Learning Multilingual Word Embeddings in Latent Metric Space: A Geometric Approach

https://github.com/migonch/unsupervised_mt

Unsupervised Machine Translation Using Monolingual Corpora Only

https://github.com/miguel-rodrigo/dot-csv-pix2pix

Image-to-Image Translation with Conditional Adversarial Networks

https://github.com/mike-a-yen/date-translation

Neural Machine Translation by Jointly Learning to Align and Translate

https://github.com/mikelasz/fair-psp

Encoding in Style: a StyleGAN Encoder for Image-to-Image Translation

https://github.com/mikomel/area-attention

Area Attention

https://github.com/milenagazdieva/extremalneuraloptimaltransport

Neural Optimal Transport

https://github.com/mim-solutions/bert_for_longer_texts

Attention Is All You Need

https://github.com/mim-solutions/roberta_for_longer_texts

Attention Is All You Need

https://github.com/mimikaan/Attention-Model

Recurrent Models of Visual Attention

https://github.com/mindslab-ai/hififace

StarGAN v2: Diverse Image Synthesis for Multiple Domains

https://github.com/mindspore-ai/models/tree/master/official/audio/melgan

MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis

https://github.com/mindspore-ai/models/tree/master/official/nlp/gnmt_v2

Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation

https://github.com/mindspore-ai/models/tree/master/official/nlp/gru

Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation

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