https://github.com/2023-MindSpore-4/Code6/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.
- 하위 태스크
- 0
- 평가표
- 1
- 보존된 평가 기록
- 2
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/2023-MindSpore-4/Code8/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/2024-MindSpore-1/Code5/tree/main/ADNet
ADNet: Attention-guided Deformable Convolutional Network for High Dynamic Range Imaging
https://github.com/AIwithSwift/TFWorld2019-SwiftIn3Hours
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
https://github.com/ANIME305/Anime-GAN
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/ANIME305/Anime-GAN-tensorflow
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/AlexVerine/PrecisionRecallGan
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/AlexeiKaplenko/FaceShifter_mine
FaceShifter: Towards High Fidelity And Occlusion Aware Face Swapping
https://github.com/AlexisAyme/estimateurs-robustes
Robust Estimators in High Dimensions without the Computational Intractability
https://github.com/Alibaba-MIIL/TResNet
https://github.com/Alien9427/SAR_specific_models
Classification of Large-Scale High-Resolution SAR Images with Deep Transfer Learning
https://github.com/AmirsSaad/UWPhysDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/AndreaBorghesi/anomaly_detection_HPC
Anomaly Detection using Autoencoders in High Performance Computing Systems
https://github.com/BalajiAJ/DenseDepth
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Bigpingping97/ICNet
ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/BlossomingL/compression_tool
https://github.com/Burf/HRNetV2-OCR-Tensorflow2
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Burf/tfdetection
https://github.com/CASIA-IVA-Lab/ISP-reID
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/CODEJIN/HiFiSinger
HiFiSinger: Towards High-Fidelity Neural Singing Voice Synthesis
https://github.com/COMP6248-Reproducability-Challenge/selfsupervised-denoising
https://github.com/CSAILVision/semantic-segmentation-pytorch
High-Resolution Representations for Labeling Pixels and Regions
https://github.com/Calylyli/Mindsporehrnet
Deep High-Resolution Representation Learning for Visual Recognition
https://github.com/ChiCheng123/SRN
Selective Refinement Network for High Performance Face Detection
https://github.com/ChristinaRunkel/HighSpeedImaging
A Bit Too Much? High Speed Imaging from Sparse Photon Counts
https://github.com/CrisalixSA/h3ds
https://github.com/DBraun/DAC-JAX
https://github.com/DLR-RM/stable-baselines3
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/Deeksha-Pandit/EVA4-S10
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/DeepGraphLearning/graphvite
GraphVite: A High-Performance CPU-GPU Hybrid System for Node Embedding
https://github.com/DonhauserK/DonhauserK.github.io
How rotational invariance of common kernels prevents generalization in high dimensions
https://github.com/EugenHotaj/pytorch-generative/blob/master/pytorch_generative/models/vae/vq_vae_2.py
https://github.com/EugeneNdiaye/smoothed_concomitant_lasso
Efficient Smoothed Concomitant Lasso Estimation for High Dimensional Regression
https://github.com/FaceOnLive/DeepFake-Detection-SDK-Linux
https://github.com/FilippoAiraldi/global-optimization
PyTorch: An Imperative Style, High-Performance Deep Learning Library
https://github.com/Francis-Hsu/alocv
Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.