https://github.com/endywon/texture-reformer
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
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/enhuiz/torchnmt
https://github.com/enhuiz/transformer-pytorch
https://github.com/enzomuschik/distilfnd
r/Fakeddit: A New Multimodal Benchmark Dataset for Fine-grained Fake News Detection
https://github.com/ercbunny/isaacgymenvs
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/eric-yim/distribution_reconstruction
https://github.com/eric-yim/image_reconstruction
https://github.com/eric-yim/vqvae
https://github.com/ericperfect/Bert_Position_BiLSTM_Attention_CRF_LSTMDecoder
https://github.com/eshaanagarwal/hr-net-implementation
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/eth-pbl/elmap-rl-controller
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/ethancaballero/neural-engineers-first-attempt
https://github.com/etjoa003/explainable_ai
Two Instances of Interpretable Neural Network for Universal Approximations
https://github.com/eunseo1092/Graduation_project
https://github.com/ewdowiak/Sicilian_Translator
https://github.com/exa-labs/exa-mcp-server
https://github.com/explainingai-code/StableDiffusion-PyTorch
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/extensive-nlp/TSAI-DeepNLP-END2.0
https://github.com/fabiankaechele/samplingfromautoencoders
Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders
https://github.com/facebookresearch/Horizon
https://github.com/facebookresearch/TensorComprehensions
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/facebookresearch/msn
High Fidelity Visualization of What Your Self-Supervised Representation Knows About
https://github.com/facebookresearch/rl/blob/main/examples/ddpg/ddpg.py
https://github.com/facebookresearch/tacto
https://github.com/facebookresearch/xformers
https://github.com/factoryIO/1-simple_neural_style_transfer
Semantic Style Transfer and Turning Two-Bit Doodles into Fine Artworks
https://github.com/faddey-w/face-swap
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/faizansuhail89/bert
https://github.com/fanchenyou/transformer-study
https://github.com/fancyerii/bert
https://github.com/fanshixiong/tracker
https://github.com/faridSam/srn
Selective Refinement Network for High Performance Face Detection
https://github.com/fashn-AI/tryondiffusion
Simple diffusion: End-to-end diffusion for high resolution images
https://github.com/fciannel/bert_fciannel
https://github.com/fd873630/RNN-Transducer
https://github.com/fdcl-gwu/gym-rotor
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.