https://github.com/dataplayer12/Fly-LSH
공식 코드Improving Similarity Search with High-dimensional Locality-sensitive Hashing
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/david-yoon/attentive-modality-hopping-for-SER
공식 코드Attentive Modality Hopping Mechanism for Speech Emotion Recognition
https://github.com/david26694/sktools
공식 코드Quantile Encoder: Tackling High Cardinality Categorical Features in Regression Problems
https://github.com/davidgolub/QuestionGeneration
공식 코드Two-Stage Synthesis Networks for Transfer Learning in Machine Comprehension
https://github.com/dawn0815/UniSA
공식 코드https://github.com/dawn0815/saeval-benchmark
공식 코드https://github.com/ddlBoJack/emotion2vec
공식 코드emotion2vec: Self-Supervised Pre-Training for Speech Emotion Representation
https://github.com/declare-lab/MELD
공식 코드MELD: A Multimodal Multi-Party Dataset for Emotion Recognition in Conversations
https://github.com/declare-lab/RECCON
공식 코드https://github.com/declare-lab/conv-emotion
공식 코드COSMIC: COmmonSense knowledge for eMotion Identification in Conversations
https://github.com/declare-lab/knot
공식 코드KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
https://github.com/declare-lab/sinkhorn-loss
공식 코드KNOT: Knowledge Distillation using Optimal Transport for Solving NLP Tasks
https://github.com/deep-mi/olf-bulb-segmentation
공식 코드Automated Olfactory Bulb Segmentation on High Resolutional T2-Weighted MRI
https://github.com/deep500/deep500
공식 코드A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
https://github.com/deeplearnxmu/tmtc
공식 코드Type-Driven Multi-Turn Corrections for Grammatical Error Correction
https://github.com/deepmind/deepmind-research
공식 코드High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/deepmind/deepmind-research/tree/master/nfnets
공식 코드High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/deepmind/jax_privacy
공식 코드Unlocking High-Accuracy Differentially Private Image Classification through Scale
https://github.com/deepmind/neural_testbed
공식 코드Evaluating High-Order Predictive Distributions in Deep Learning
https://github.com/deepmind/sonnet
공식 코드https://github.com/deepskies/deepmerge-public
공식 코드DeepMerge: Classifying High-redshift Merging Galaxies with Deep Neural Networks
https://github.com/deepvk/emospeech
공식 코드EmoSpeech: Guiding FastSpeech2 Towards Emotional Text to Speech
https://github.com/deezer/CrossCulturalMusicGenrePerception
공식 코드Modeling the Music Genre Perception across Language-Bound Cultures
https://github.com/deqingyang/cisper
공식 코드Contextual Information and Commonsense Based Prompt for Emotion Recognition in Conversation
https://github.com/desmond-ong/TAC-EA-model
공식 코드Modeling emotion in complex stories: the Stanford Emotional Narratives Dataset
https://github.com/dexterdley/MorphSet
공식 코드Morphset:Augmenting categorical emotion datasets with dimensional affect labels using face morphing
https://github.com/dgurnari/knotsbm
공식 코드https://github.com/dh7401/rf-disagreement
공식 코드https://github.com/dholzmueller/nn_inconsistency
공식 코드Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent
https://github.com/dicarlolab/cornet
공식 코드Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
https://github.com/dicarlolab/neurips2019
공식 코드Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs
https://github.com/diegovalsesia/hyperspectral-dequantization
공식 코드High-throughput Onboard Hyperspectral Image Compression with Ground-based CNN Reconstruction
https://github.com/difanliu/asset
공식 코드ASSET: Autoregressive Semantic Scene Editing with Transformers at High Resolutions
https://github.com/dineshsinghindian/fsnet
공식 코드FsNet: Feature Selection Network on High-dimensional Biological Data
https://github.com/dingmyu/HR-NAS
공식 코드HR-NAS: Searching Efficient High-Resolution Neural Architectures with Lightweight Transformers
https://github.com/diogocortiz/portugueseemotionrecognitionweaksupervision
공식 코드A Weakly Supervised Dataset of Fine-Grained Emotions in Portuguese
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.