https://github.com/tsc2017/frechet-inception-distance
GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium
Papers with Code 아카이브
Emotion Recognition
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/tshev/faster-FastText
https://github.com/tshu-w/dbcopilot
https://github.com/tshu-w/llm4em
https://github.com/tugstugi/mongolian-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/tvinith/bert
https://github.com/txyugood/Restormer_Paddle
Restormer: Efficient Transformer for High-Resolution Image Restoration
https://github.com/tyxr/bert
https://github.com/tzirakis/Multimodal-Emotion-Recognition
End-to-End Multimodal Emotion Recognition using Deep Neural Networks
https://github.com/u7javed/Transformer-Multi-Language-Translator
https://github.com/uabinf/nlp-fall-2019-project-shuhui_shuqu
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ubc-vision/DwNet
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ucabcg3/msc_bias_llm_project
Angry Men, Sad Women: Large Language Models Reflect Gendered Stereotypes in Emotion Attribution
https://github.com/ufal/wembedding_service
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/umeiko/mindspore-seq2seq
https://github.com/unik00/textCNN-pytorch
https://github.com/unik00/textCNN-tf2
https://github.com/uoguelph-mlrg/instance_selection_for_gans
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/usama6832/attention_model
https://github.com/ustcljw/fupugec-score
https://github.com/utterworks/fast-bert
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uva-dsa/ems-pipeline
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uvictor-git/fake_news_detection
Deep Two-path Semi-supervised Learning for Fake News Detection
https://github.com/uygarkurt/BERT-PyTorch
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/uzh-rpg/rpg_e2vid
High Speed and High Dynamic Range Video with an Event Camera
https://github.com/uzi0espil/research-papers-implementation/tree/master/Attention%20Is%20All%20You%20Need
https://github.com/uzkent/EfficientObjectDetection
HHHFL: Hierarchical Heterogeneous Horizontal Federated Learning for Electroencephalography
https://github.com/v-iashin/SpecVQGAN
https://github.com/v1viswan/Domain_adaptation_in_HRNet
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/vainf/diff-pruning
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/valentinmace/noisy-text
https://github.com/vanpersie32/Multigpu-Bert
https://github.com/vanslerry/long_text
https://github.com/varun-affinsys/Monocular-Depth-Estimation-with-Transfer-Learning-pretrained-MobileNetV2
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/vballoli/nfnets-pytorch
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/veekaybee/what_are_embeddings
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.