Papers with Code 아카이브

Emotion Recognition

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하위 태스크: 12
평가표: 10
보존된 평가 기록: 18

Emotion Recognition is an important area of research to enable effective human-computer interaction. Human emotions can be detected using speech signal, facial expressions, body language, and electroencephalography (EEG). Source: Using Deep Autoencoders for Facial Expression Recognition

코드

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

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

Attention Is All You Need

https://github.com/taotaonice/FaceShifter

FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping

https://github.com/tatp22/linformer-pytorch

Attention Is All You Need

https://github.com/tatp22/multidim-positional-encoding

Attention Is All You Need

https://github.com/tbmoon/LANL_Earthquake_Prediction

Attention Is All You Need

https://github.com/tcnguyen/bert

Attention Is All You Need

https://github.com/tcxdgit/MachineTranslation_t2t

Attention Is All You Need

https://github.com/tdd-ai/data-collection

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/tdmeeste/SparseSeqModels

Breaking the Softmax Bottleneck: A High-Rank RNN Language Model

https://github.com/tegg89/DLCamp_Jeju2018

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnet

Continuous control with deep reinforcement learning

https://github.com/tegg89/magnn

Continuous control with deep reinforcement learning

https://github.com/tegg89/mann

Continuous control with deep reinforcement learning

https://github.com/teghub/TurkishNER-BERT

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/tejaswigowda/semseg-pytorch

High-Resolution Representations for Labeling Pixels and Regions

https://github.com/tensorflow/models/tree/master/official/nlp

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/tensorlayer/RLzoo

Continuous control with deep reinforcement learning

https://github.com/tensorops/TransformerX

Attention Is All You Need

https://github.com/tensorpack/tensorpack/tree/master/examples/FasterRCNN

Cascade R-CNN: Delving into High Quality Object Detection

https://github.com/terbed/Deep-rPPG

Remote Photoplethysmograph Signal Measurement from Facial Videos Using Spatio-Temporal Networks

https://github.com/terenceylchow124/Meme-MultiModal

SemEval-2020 Task 8: Memotion Analysis -- The Visuo-Lingual Metaphor!

https://github.com/text-machine-lab/transformerpy

Attention Is All You Need

https://github.com/text-to-audio/make-an-audio

ProDiff: Progressive Fast Diffusion Model For High-Quality Text-to-Speech

https://github.com/tgisaturday/dalle-lightning

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/tgisaturday/dalle-lightning-tpu

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/tgisaturday/taming-transformers-tpu

Generating Diverse High-Fidelity Images with VQ-VAE-2

https://github.com/thainv0212/re-ddpg

Continuous control with deep reinforcement learning

https://github.com/thanhlong1997/bert_quora

Attention Is All You Need

https://github.com/the-Quert/iNLPfun

Attention Is All You Need

https://github.com/theQuert/inlpfun

Attention Is All You Need

https://github.com/thecodemasterk/BERT

Attention Is All You Need

https://github.com/theneuralbeing/bert-finetuning-webinar

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/thethirdwheel/malumagraph

BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding

https://github.com/theziqi/dccc

Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net

https://github.com/thiagorjes/depth_estimation

High Quality Monocular Depth Estimation via Transfer Learning

https://github.com/thisisiron/kaggle-toxic-comment

Convolutional Neural Networks for Sentence Classification

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