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/xiangyue9607/CliniRC

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

https://github.com/xiaoiker/meta_dpm

Taming Transformers for High-Resolution Image Synthesis

https://github.com/xiaopingzhong/bert-finetune-for-classfier

Attention Is All You Need

https://github.com/xiaopp123/bert_explain

Attention Is All You Need

https://github.com/xiaoxinyu1997/Knowledge-Graph

Convolutional Neural Networks for Sentence Classification

https://github.com/xingchensong/s3tokenizer

FunAudioLLM: Voice Understanding and Generation Foundation Models for Natural Interaction Between Humans and LLMs

https://github.com/xingjian-zhang/massw

Attention Is All You Need

https://github.com/xingniu/sockeye

Attention Is All You Need

https://github.com/xiongjiechen/ASWD

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/xitianxiaofeixue/BERT

Attention Is All You Need

https://github.com/xiuyu0000/new_papers_codes/tree/main/Pix2PixHD

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/xiuyu0000/new_papers_codes/tree/main/bert

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

https://github.com/xiuyu0000/papers_with_examples/tree/main/Lite-HRNet

Lite-HRNet: A Lightweight High-Resolution Network

https://github.com/xiuyu0000/tutorials/tree/main/bert

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

https://github.com/xmu-xiaoma666/External-Attention-pytorch

Attention Is All You Need

https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/attention/PolarizedSelfAttention.py

Polarized Self-Attention: Towards High-quality Pixel-wise Regression

https://github.com/xtma/simple-pytorch-rl

Continuous control with deep reinforcement learning

https://github.com/xuyuandong/simple-ddpg

Continuous control with deep reinforcement learning

https://github.com/xuzhezhaozhao/bert_reading

Attention Is All You Need

https://github.com/xuzhezhaozhao/fastText_reading

Bag of Tricks for Efficient Text Classification

https://github.com/xwshen51/AGE

PacGAN: The power of two samples in generative adversarial networks

https://github.com/xwshen51/AGES

PacGAN: The power of two samples in generative adversarial networks

https://github.com/xycheng/net_logit_test

Classification Logit Two-sample Testing by Neural Networks

https://github.com/xydaytoy/BMI-NMT

Attention Is All You Need

https://github.com/xyfJASON/Comment-Analysis

Bag of Tricks for Efficient Text Classification

https://github.com/xypu98/two-stage-HDR-video

A Two-stage Deep Network for High Dynamic Range Image Reconstruction

https://github.com/xyshadow/baseline_ddpg

Continuous control with deep reinforcement learning

https://github.com/xzt008/standard-gp-is-all-you-need-for-hdbo

High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces

https://github.com/yajus/ueca-prompt

https://github.com/yakhyo/faceboxes-pytorch

FaceBoxes: A CPU Real-time Face Detector with High Accuracy

https://github.com/yangfeifei02/HOSP

High-order structure preserving graph neural network for few-shot learning

https://github.com/yanggeng1995/GAN-TTS

High Fidelity Speech Synthesis with Adversarial Networks

https://github.com/yangling0818/contextdiff

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/yangyucheng000/CRA

Contextual Residual Aggregation for Ultra High-Resolution Image Inpainting

https://github.com/yangyucheng000/HRNetW48_cls

Deep High-Resolution Representation Learning for Visual Recognition

https://github.com/yangyucheng000/ICT

High-Fidelity Pluralistic Image Completion with Transformers

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