https://github.com/alibaba-damo-academy/funcodec
LauraGPT: Listen, Attend, Understand, and Regenerate Audio with GPT
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/alibaba-miil/peta
Recognizing and Curating Photo Albums via Event-Specific Image Importance
https://github.com/alibaba/EasyCV
https://github.com/alibaba/EasyNLP
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/alibaba/EasyNLP/tree/master/examples/text2image_generation
https://github.com/alibaba/cluster-contrast
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/alibaba/cluster-contrast-reid
Two at Once: Enhancing Learning and Generalization Capacities via IBN-Net
https://github.com/alibabasglab/frcrn
https://github.com/alililia/ascend_HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/alililia/ascend_RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
https://github.com/alililia/ms_contrib/tree/main/ascend_transformer
https://github.com/alililia/ms_contrib/tree/main/gpu_transformer
https://github.com/alimpiev/embeddings_persistent_homology
https://github.com/alinstein/Depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/aliyzd95/modified_shemo
https://github.com/altomator/Introduction_to_Deep_Learning-2-Face_Detection
A Century of Portraits: A Visual Historical Record of American High School Yearbooks
https://github.com/amanjaiswal73892/change_my_pet
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/amauriciorr/AubreyBot
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/ambityga/memsem
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/amirsh15/cross_modality_graph
https://github.com/amirsh15/vaed_hetergraph
https://github.com/amy-tabb/aruco-pattern-write
Using cameras for precise measurement of two-dimensional plant features: CASS
https://github.com/amymariaparker2401/new
https://github.com/ancastillar/A-high-bias-low-variance-introduction-to-Machine-Learning-for-physicists.-
A high-bias, low-variance introduction to Machine Learning for physicists
https://github.com/andi611/Mockingjay-Speech-Representation
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
https://github.com/andreidi/AC_DDPG_walker
https://github.com/andrewfwalters/w266-final-project
https://github.com/anhinga/JuliaCon2021-poster
https://github.com/anhtu293/transformer_from_scratch
https://github.com/aniketfadia96/Convolutional-Neural-Networks-for-Text-Classification
https://github.com/aniruddha343/Two-stream-Encoder-Decoder-Network-for-Image-Forensics
Two-stream Encoder-Decoder Network for Localizing Image Forgeries
https://github.com/anirudharc/transformer
https://github.com/anishacharya/BGMD
Robust Training in High Dimensions via Block Coordinate Geometric Median Descent
https://github.com/anishathalye/Guided-Denoise
Defense against Adversarial Attacks Using High-Level Representation Guided Denoiser
https://github.com/anishnarkar/experiments_deeplearning_models
https://github.com/anita-hu/TF2-RL
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.