https://github.com/fdu-me-arc/approxflow
공식 코드HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
Papers with Code 아카이브
Emotion Recognition
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- 평가표
- 10
- 보존된 평가 기록
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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/fdu-me-arc/heam
공식 코드HEAM: High-Efficiency Approximate Multiplier Optimization for Deep Neural Networks
https://github.com/feichtenhofer/twostreamfusion
공식 코드Two-Stream Convolutional Networks for Action Recognition in Videos
https://github.com/feizhe/HDCQR_Paper
공식 코드https://github.com/felixbenning/masterthesis
공식 코드High Dimensional Optimization through the Lens of Machine Learning
https://github.com/felixhol/biteOscope
공식 코드https://github.com/felixrosberg/facedancer
공식 코드FaceDancer: Pose- and Occlusion-Aware High Fidelity Face Swapping
https://github.com/fengju514/Expression-Net
공식 코드https://github.com/fengliu90/DK-for-TST
공식 코드https://github.com/fengliu90/MetaTesting
공식 코드Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data
https://github.com/fengyi233/depth-to-normal-translator
공식 코드https://github.com/filby89/NTUA-BEEU-eccv2020
공식 코드Emotion Understanding in Videos Through Body, Context, and Visual-Semantic Embedding Loss
https://github.com/financialcomputingucl/drl_for_active_high_frequency_trading
공식 코드Deep Reinforcement Learning for Active High Frequency Trading
https://github.com/fiyud/Emotional-Vietnamese-Speech-Based-Depression-Diagnosis-Using-Dynamic-Attention-Mechanism
공식 코드Emotional Vietnamese Speech-Based Depression Diagnosis Using Dynamic Attention Mechanism
https://github.com/fjxmlzn/PacGAN
공식 코드PacGAN: The power of two samples in generative adversarial networks
https://github.com/florentincdx/swot-flow
공식 코드Learning Optimal Transport Between two Empirical Distributions with Normalizing Flows
https://github.com/fontanf/packingsolver
공식 코드https://github.com/for-a-few-dpps-more/magneticlaplaciansparsifier.jl
공식 코드Sparsification of the regularized magnetic Laplacian with multi-type spanning forests
https://github.com/frankaging/ACII2019-transformer
공식 코드https://github.com/fredbuhl/emogator
공식 코드https://github.com/free1dom1/tbformer
공식 코드TBFormer: Two-Branch Transformer for Image Forgery Localization
https://github.com/freedompuppy77/charon
공식 코드Mamba-VA: A Mamba-based Approach for Continuous Emotion Recognition in Valence-Arousal Space
https://github.com/frozentoad9/CMST
공식 코드Prabhupadavani: A Code-mixed Speech Translation Data for 25 Languages
https://github.com/frozoul/4k-nerf
공식 코드4K-NeRF: High Fidelity Neural Radiance Fields at Ultra High Resolutions
https://github.com/fruttasecca/concept-tagging-with-neural-networks
공식 코드Concept Tagging for Natural Language Understanding: Two Decadelong Algorithm Development
https://github.com/fufangze/SDR-GNN
공식 코드https://github.com/funaudiollm/cosyvoice
공식 코드https://github.com/gathanei/xyz
공식 코드The xyz algorithm for fast interaction search in high-dimensional data
https://github.com/gchochla/demux-memo
공식 코드Leveraging Label Correlations in a Multi-label Setting: A Case Study in Emotion
https://github.com/gdebarghya/Network-TwoSampleTesting
공식 코드https://github.com/gengshan-y/rigidmask
공식 코드https://github.com/geomagical/lama-with-refiner
공식 코드Feature Refinement to Improve High Resolution Image Inpainting
https://github.com/georgestein/supaernova
공식 코드A Probabilistic Autoencoder for Type Ia Supernovae Spectral Time Series
https://github.com/georgewynne/Kernel-Functional-Data
공식 코드https://github.com/gguibon/protoseq
공식 코드Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks
https://github.com/gioannides/gaussian-adaptive-attention
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.