https://github.com/chiang9/NLP-Chinese_couplet_generation
AnchiBERT: A Pre-Trained Model for Ancient ChineseLanguage Understanding and Generation
Papers with Code 아카이브
Speech Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
- 4
- 평가표
- 17
- 보존된 평가 기록
- 48
Speech Emotion Recognition is a task of speech processing and computational paralinguistics that aims to recognize and categorize the emotions expressed in spoken language. The goal is to determine the emotional state of a speaker, such as happiness, anger, sadness, or frustration, from their speech patterns, such as prosody, pitch, and rhythm.
For multimodal emotion recognition, please upload your result to Multimodal Emotion Recognition on IEMOCAP
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/chiayewken/bert-qa
https://github.com/chinhsuanwu/dreamfusionacc
https://github.com/chiukin/SPANet
Spatial Attentive Single-Image Deraining with a High Quality Real Rain Dataset
https://github.com/chrisleunglokhin/Capstone-BERT
https://github.com/christianspybrook/article_recommender
https://github.com/chuanqichen/FakeNewsDetection
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
https://github.com/chuanqichen/deepcoaching
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/clabrugere/final-mlp
FinalMLP: An Enhanced Two-Stream MLP Model for CTR Prediction
https://github.com/clarken92/Mixed-variate-RBM
Outlier Detection on Mixed-Type Data: An Energy-based Approach
https://github.com/clarken92/vfm
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/claudeHifly/BipedalWalker-v3
https://github.com/cloudmcloudyo/capstone
https://github.com/clu0/unet.cu
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/cmsflash/efficient-attention
https://github.com/coco60/bert-test
https://github.com/code-implementation1/Code3/tree/main/faceboxes
https://github.com/code-implementation1/Code4/tree/main/HRNetV2
https://github.com/code-implementation1/Code4/tree/main/HRNetW48_cls
https://github.com/code-implementation1/Code4/tree/main/ibnnet
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/code-implementation1/Code6/tree/main/PyramidBox
PyramidBox++: High Performance Detector for Finding Tiny Face
https://github.com/code-implementation1/Code7/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/code-implementation1/Code9/tree/main/Efficientnet
EfficientHRNet: Efficient Scaling for Lightweight High-Resolution Multi-Person Pose Estimation
https://github.com/collabora/whisperspeech
https://github.com/colorfulscoop/tfdlg
https://github.com/compstorylab/contagiograms
https://github.com/compstorylab/covid19ngrams
https://github.com/compstorylab/storywrangling
https://github.com/compvis/fm-boosting
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/compvis/stable-diffusion
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/conceptofmind/LaMDA-pytorch
https://github.com/conceptofmind/lamda-rlhf-pytorch
https://github.com/conor-horgan/spectrai
High-throughput molecular imaging via deep learning enabled Raman spectroscopy
https://github.com/coronazap/bert_client
https://github.com/cosmoquester/seq2seq
https://github.com/cospplay/bert-master
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.