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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/iamalexkorotin/KernelNeuralOptimalTransport

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/iamalexkorotin/Wasserstein2Benchmark

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/iamalexkorotin/wassersteiniterativenetworks

GANs Trained by a Two Time-Scale Update Rule Converge to a Local Nash Equilibrium

https://github.com/iamhoushiyou/tsn

Temporal Segment Networks: Towards Good Practices for Deep Action Recognition

https://github.com/iamjeff7/j-va-aagcn

View Adaptive Neural Networks for High Performance Skeleton-based Human Action Recognition

https://github.com/ibaiGorordo/ONNX-SAM2-Segment-Anything

SAM 2: Segment Anything in Images and Videos

https://github.com/ibaiGorordo/ONNX-YOLOv7-Object-Detection

YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors

https://github.com/idea-research/grounded-sam-2

SAM 2: Segment Anything in Images and Videos

https://github.com/idealo/image-quality-assessment

NIMA: Neural Image Assessment

https://github.com/idealo/image-super-resolution

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/idearibosome/tf-perceptual-eusr

The 2018 PIRM Challenge on Perceptual Image Super-resolution

https://github.com/idilesenzulfikar/UNOVOST

UnOVOST: Unsupervised Offline Video Object Segmentation and Tracking

https://github.com/iduta/pyconvsegnet

Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

https://github.com/if-loops/adaptive-selective-synaptic-dampening

Parameter-tuning-free data entry error unlearning with adaptive selective synaptic dampening

https://github.com/igloooo/weather-forecasting-video-prediction

https://github.com/ihaeyong/pfnr

NeRV: Neural Representations for Videos

https://github.com/ihaeyong/pnr

NeRV: Neural Representations for Videos

https://github.com/imanlab/deep_movement_primitives

PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation

https://github.com/imatge-upc/3D-GAN-superresolution

Real-Time Single Image and Video Super-Resolution Using an Efficient Sub-Pixel Convolutional Neural Network

https://github.com/imatge-upc/refvos

RefVOS: A Closer Look at Referring Expressions for Video Object Segmentation

https://github.com/impiga/plain-detr

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/imrahulr/adversarial_robustness_pytorch

Towards Deep Learning Models Resistant to Adversarial Attacks

https://github.com/inhwanbae/npsn

Social-STGCNN: A Social Spatio-Temporal Graph Convolutional Neural Network for Human Trajectory Prediction

https://github.com/innat/UniFormerV2

UniFormerV2: Spatiotemporal Learning by Arming Image ViTs with Video UniFormer

https://github.com/innat/Video-FocalNets

Video-FocalNets: Spatio-Temporal Focal Modulation for Video Action Recognition

https://github.com/innat/VideoMAE

VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training

https://github.com/innat/VideoSwin

Video Swin Transformer

https://github.com/insomnia94/ARG

Adaptive ROI Generation for Video Object Segmentation Using Reinforcement Learning

https://github.com/ipmi-icns-uke/sparsam

Emerging Properties in Self-Supervised Vision Transformers

https://github.com/irh-ca-team-car/attention-data

BDD100K: A Diverse Driving Dataset for Heterogeneous Multitask Learning

https://github.com/irsisyphus/reconet

ReCoNet: Real-time Coherent Video Style Transfer Network

https://github.com/isaaccorley/hydro-foundation-model

Swin Transformer V2: Scaling Up Capacity and Resolution

https://github.com/isaaccorley/torchrs

Multi-image Super Resolution of Remotely Sensed Images using Residual Feature Attention Deep Neural Networks

https://github.com/isaacschaal/SG_training

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/isno0907/sosampler

Scalable Frame Sampling for Video Classification: A Semi-Optimal Policy Approach with Reduced Search Space

https://github.com/isotlaboratory/ml4vrp

Graph Attention Networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.