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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/tehret/vbm3d
Implementation of the VBM3D Video Denoising Method and Some Variants
https://github.com/tejas-gokhale/AGAT
Test-Time Training with Self-Supervision for Generalization under Distribution Shifts
https://github.com/tejgvsl/Camera-motion-classification-in-a-video-file
Modeling Spatial-Temporal Clues in a Hybrid Deep Learning Framework for Video Classification
https://github.com/tencent-hunyuan/hunyuancustom
HunyuanCustom: A Multimodal-Driven Architecture for Customized Video Generation
https://github.com/tencent-hunyuan/hunyuanvideo
HunyuanVideo: A Systematic Framework For Large Video Generative Models
https://github.com/tencent/flashvdm
TripoSG: High-Fidelity 3D Shape Synthesis using Large-Scale Rectified Flow Models
https://github.com/tengdahan/temporalalignnet
https://github.com/tensorflow/cleverhans
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/tensorflow/models/tree/archive/research/tcn
Time-Contrastive Networks: Self-Supervised Learning from Video
https://github.com/tensorflow/models/tree/master/research/audioset
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/feelvos
FEELVOS: Fast End-to-End Embedding Learning for Video Object Segmentation
https://github.com/tensorflow/models/tree/master/research/object_detection
Context R-CNN: Long Term Temporal Context for Per-Camera Object Detection
https://github.com/tensorflow/models/tree/master/research/slim
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tensorflow/models/tree/master/research/tcn
Time-Contrastive Networks: Self-Supervised Learning from Video
https://github.com/tensorflow/models/tree/master/research/video_prediction
Unsupervised Learning for Physical Interaction through Video Prediction
https://github.com/tensorworksio/mot.cpp
https://github.com/terrychenism/OctaveConv
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/tetrachrome/subpixel
https://github.com/text-to-audio/make-an-audio
Make-An-Audio: Text-To-Audio Generation with Prompt-Enhanced Diffusion Models
https://github.com/thaitruongan/camera-surveillance-ai
https://github.com/thakurnupur/Sparse-Adversarial-Perturbations-PyTorch
https://github.com/thanhtrunghuynh93/pygcn
Semi-Supervised Classification with Graph Convolutional Networks
https://github.com/theAIGuysCode/yolov4-deepsort
Simple Online and Realtime Tracking with a Deep Association Metric
https://github.com/thefonseca/predictive-coding
Deep Predictive Coding Networks for Video Prediction and Unsupervised Learning
https://github.com/thestephencasper/latent_adversarial_training
Latent Adversarial Training Improves Robustness to Persistent Harmful Behaviors in LLMs
https://github.com/thiagoambiel/NeuralAtlases
https://github.com/thiagorjes/depth_estimation
High Quality Monocular Depth Estimation via Transfer Learning
https://github.com/thomas-schillaci/SimPLe
https://github.com/thomashopkins32/PGDAdversarialLearning
Towards Deep Learning Models Resistant to Adversarial Attacks
https://github.com/thstkdgus35/EDSR-PyTorch
https://github.com/thughost2/Padam
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/tianfr/mononerf
https://github.com/tianfr/semantic-flow
https://github.com/tianhai123/MIM
https://github.com/tianhai123/conv-gru
Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.