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/avinashsai/Convolutional-Neural-Networks-for-Text-Classification

Convolutional Neural Networks for Sentence Classification

https://github.com/avishek-018/single_notebook_projects

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

https://github.com/avivelor/UnityMachineLearningForProjectButterfly

Continuous control with deep reinforcement learning

https://github.com/awesomericky/Speech2Pickup

Audio-Linguistic Embeddings for Spoken Sentences

https://github.com/aws-samples/amazon-sagemaker-bert-pytorch

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

https://github.com/awslabs/mlm-scoring

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

https://github.com/awslabs/sockeye

Attention Is All You Need

https://github.com/ayanglab/cs2

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/ayanglab/hdl

High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs

https://github.com/ays-dev/keras-transformer

Attention Is All You Need

https://github.com/ayulockin/Explore-NFNet

High-Performance Large-Scale Image Recognition Without Normalization

https://github.com/b06b01073/continuous-control

Continuous control with deep reinforcement learning

https://github.com/bGhorbani/Lazy-Training-Neural-Nets

Limitations of Lazy Training of Two-layers Neural Networks

https://github.com/baaesh/semeval19_task3

https://github.com/baaivision/vid2vid-zero

High-Resolution Image Synthesis with Latent Diffusion Models

https://github.com/babelscape/wikineural

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

https://github.com/bacdavid/HER

Continuous control with deep reinforcement learning

https://github.com/backgom2357/Recommender_system_via_deep_RL

Continuous control with deep reinforcement learning

https://github.com/bagustris/Apsipa2019_SpeechText

https://github.com/bagustris/a-vb2022

The ACII 2022 Affective Vocal Bursts Workshop & Competition: Understanding a critically understudied modality of emotional expression

https://github.com/bagustris/multistage-ser

Multitask Learning and Multistage Fusion for Dimensional Audiovisual Emotion Recognition

https://github.com/baiyang2464/chatbot-base-on-Knowledge-Graph

Convolutional Neural Networks for Sentence Classification

https://github.com/bakerv/fer-webcam

Facial Emotion Recognition: State of the Art Performance on FER2013

https://github.com/bangoc123/transformer

Attention Is All You Need

https://github.com/baokuiwang/context_aware_situation_entity

Building Context-aware Clause Representations for Situation Entity Type Classification

https://github.com/baoshengyu/deep-high-resolution-net.pytorch

Deep High-Resolution Representation Learning for Human Pose Estimation

https://github.com/baosongyang/WRD

Attention Is All You Need

https://github.com/baotramduong/Sentiment-Analysis-with-Deep-Learning-using-BERT

Attention Is All You Need

https://github.com/baotramduong/Twitter-Sentiment-Analysis-with-Deep-Learning-using-BERT

Attention Is All You Need

https://github.com/baoyujing/hdmi

HDMI: High-order Deep Multiplex Infomax

https://github.com/bartzi/chainer-transformer

Attention Is All You Need

https://github.com/basile-bron/Artificial-Intelligence-Lexical

Attention Is All You Need

https://github.com/baturaysaglam/RIS-MISO-Deep-Reinforcement-Learning

Continuous control with deep reinforcement learning

https://github.com/baturaysaglam/ac-off-poc

Continuous control with deep reinforcement learning

https://github.com/baturaysaglam/dase

Continuous control with deep reinforcement learning

https://github.com/bbonik/facial-expression-analysis

Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing

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