https://github.com/WeLoveKiraboshi/In-PlaneRotation-AwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
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/WeLoveKiraboshi/In-PlaneRotationAwareDepthEstimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/Westlake-AI/openmixup
HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions
https://github.com/XiaXuehai/faceboxes
https://github.com/Young-in/ANM-Assignment2-loglizer
Anomaly Detection using Autoencoders in High Performance Computing Systems
https://github.com/Yu-Group/iterative-Random-Forest
Iterative Random Forests to detect predictive and stable high-order interactions
https://github.com/YvanG/VQGAN-CLIP
https://github.com/ZC119/richardson_tracking
https://github.com/ZVK/Talking-Heads
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/ZVK/talking_heads
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/a-vzer/waveletflowpytorch
Wavelet Flow: Fast Training of High Resolution Normalizing Flows
https://github.com/a414351664/my_git_laser
https://github.com/aakashrkaku/seq2seq_hrar
Sequence-to-Sequence Modeling for Action Identification at High Temporal Resolution
https://github.com/abhi1kumar/hrnet_pose_single_gpu
Deep High-Resolution Representation Learning for Human Pose Estimation
https://github.com/abhi8585/GeneratedART-NFT-VISION
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/adik993/ppo-pytorch
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/advimman/lama
Feature Refinement to Improve High Resolution Image Inpainting
https://github.com/agermanidis/pix2pixHD-runway
High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs
https://github.com/ahsanmemon/NFNets-Tensorflow
High-Performance Large-Scale Image Recognition Without Normalization
https://github.com/ai-forever/Kandinsky-2
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
https://github.com/ai-forever/kandinskyvideo
Align your Latents: High-Resolution Video Synthesis with Latent Diffusion Models
https://github.com/ai-forever/movqgan
MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation
https://github.com/ai4finance-foundation/finrl-meta
Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning
https://github.com/aim-uofa/AdelaiDet
BoxInst: High-Performance Instance Segmentation with Box Annotations
https://github.com/aim-uofa/adet
BoxInst: High-Performance Instance Segmentation with Box Annotations
https://github.com/aimbsg/EVA4_S15
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/ajbrock/BigGAN-PyTorch
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/akoepke/audio-retrieval-benchmark
QuerYD: A video dataset with high-quality text and audio narrations
https://github.com/akreiss/HighDimRD
Inference in Regression Discontinuity Designs with High-Dimensional Covariates
https://github.com/albertopolito/CarSNN
Spatio-Temporal Backpropagation for Training High-performance Spiking Neural Networks
https://github.com/alecfilios/Training-Intelligent-game-Agents-through-Competitive-Reinforcement-Learning
High-Dimensional Continuous Control Using Generalized Advantage Estimation
https://github.com/alexverine/precisionrecallbiggan
Large Scale GAN Training for High Fidelity Natural Image Synthesis
https://github.com/alialaradi/DeepGalerkinMethod
Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning
https://github.com/alibaba/EasyCV
https://github.com/alibaba/EasyNLP/tree/master/examples/text2image_generation
https://github.com/alililia/ascend_HRNetW48_cls
Deep High-Resolution Representation Learning for Visual Recognition
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.