https://github.com/MindSpore-paper-code-3/code5/tree/main/RefineNet
RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation
Papers with Code 아카이브
Vocal Bursts Intensity Prediction
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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predict the intensity of 10 categorical emotions
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-paper-code-3/code6/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/MindSpore-paper-code-3/code7/tree/main/faceboxes
https://github.com/MindSpore-scientific-2/code-14/tree/main/marian
Marian: Cost-effective High-Quality Neural Machine Translation in C++
https://github.com/MindSpore-scientific/code-11/tree/main/Super-Resolution-for-Root-Imaging
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/Mleader2/text_scalpel
https://github.com/Mohanned-Elkholy/BigGan-model-inversion-with-image-transformations
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/MoonInTheRiver/DiffSinger
PortaSpeech: Portable and High-Quality Generative Text-to-Speech
https://github.com/MoritzKappel/HF-NHMT
High-Fidelity Neural Human Motion Transfer from Monocular Video
https://github.com/NERSC/sc21-dl-tutorial
Fast, high-fidelity Lyman $α$ forests with convolutional neural networks
https://github.com/NSR9/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NU-LL/lighttrack-
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NUISTGY/Deep-High-Resolution-Representation-Learning-for-Cross-Resolution-Person-Re-identification
Deep High-Resolution Representation Learning for Cross-Resolution Person Re-identification
https://github.com/NVIDIA-Omniverse/IsaacGymEnvs
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/NVIDIA/MinkowskiEngine
High-dimensional Convolutional Networks for Geometric Pattern Recognition
https://github.com/NVIDIA/unsupervised-video-interpolation
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation
https://github.com/NVlabs/PAMTRI
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/NVlabs/industreallib
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/NVlabs/selfsupervised-denoising
https://github.com/NiallEHunt/MonocularDepth-Using-LightFields
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/NiallJeffrey/DeepMass
Solving high-dimensional parameter inference: marginal posterior densities & Moment Networks
https://github.com/Nikolai10/MRIC
https://github.com/Noopuragr/DepthModel
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Olksndr/FaceShifter
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/OsvaldN/APS360_Project
Adversarial Training of Variational Auto-encoders for High Fidelity Image Generation
https://github.com/PacktPublishing/Hands-On-Image-Generation-with-TensorFlow-2.0/tree/master/Chapter08
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/PaddlePaddle/PaddleClas
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/PaddlePaddle/PaddleDetection
https://github.com/PaddlePaddle/PaddleNLP/tree/develop/ppdiffusers
High-Resolution Image Synthesis with Latent Diffusion Models
https://github.com/PaddlePaddle/PaddleSpeech
StyleMelGAN: An Efficient High-Fidelity Adversarial Vocoder with Temporal Adaptive Normalization
https://github.com/Paulpauladq/HighSpeedFaceTracker
https://github.com/PhilippBach/hdm
https://github.com/PhilippBach/hdm_prev
https://github.com/PhilippeNguyen/keras_wavenet
https://github.com/QEDan/links_clustering
https://github.com/QiaoLiuHit/LSOTB-TIR
LSOTB-TIR:A Large-Scale High-Diversity Thermal Infrared Object Tracking Benchmark
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.