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

https://github.com/farhadsh1992/stlpips_metric_tflite

Shift-tolerant Perceptual Similarity Metric

https://github.com/farrell236/SVRnet

Predicting Slice-to-Volume Transformation in Presence of Arbitrary Subject Motion

https://github.com/farrowlab/Neuron-Softsegmentation

Modular proximal optimization for multidimensional total-variation regularization

https://github.com/fastiqa/fastiqa

From Patches to Pictures (PaQ-2-PiQ): Mapping the Perceptual Space of Picture Quality

https://github.com/fcUalberta/UAlberta-Multimedia-Masters-Program-Interpretable-AI-Part_1_2

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

https://github.com/fcakir/deep-mihash

Hashing with Mutual Information

https://github.com/fchollet/deep-learning-models

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/fdsa1860/skeletal

https://github.com/febert/neuralproposal_cem_classproject

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

https://github.com/febert/robustness_via_retrying

Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning

https://github.com/fenghansen/ESRGAN-Keras

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/fengwang/subpixel_conv2d

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

https://github.com/fengyang95/pyCFTrackers

Staple: Complementary Learners for Real-Time Tracking

https://github.com/ferseiti/reproducibility

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/fgabel/EfficientDet-DeepSORT-Tracker

Simple Online and Realtime Tracking with a Deep Association Metric

https://github.com/fiabdu/Commonly-Interesting-Images

VILA: Learning Image Aesthetics from User Comments with Vision-Language Pretraining

https://github.com/fikratgasimov/Generative_Adversial

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

https://github.com/final-0/Real-ESRGAN

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/final-0/Real-ESRGAN-bicubic

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks

https://github.com/final-0/icm-v1

Image Coding for Machines with Edge Information Learning Using Segment Anything

https://github.com/firework8/ProtoGCN

Revealing Key Details to See Differences: A Novel Prototypical Perspective for Skeleton-based Action Recognition

https://github.com/fluque1995/tfm-anomaly-detection

Real-world Anomaly Detection in Surveillance Videos

https://github.com/fnando1995/INTEL_Project

Simple Online and Realtime Tracking

https://github.com/fniroui/depth2room

Video-to-Video Synthesis

https://github.com/fongyk/graph-attention

Graph Attention Networks

https://github.com/foolwood/SiamMask

Fast Online Object Tracking and Segmentation: A Unifying Approach

https://github.com/fourseaforfriend/waifu2x

Image Super-Resolution Using Deep Convolutional Networks

https://github.com/francislata/Sign-Language-Digits-CNN

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/francois-rozet/piqa

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

https://github.com/frankaging/pyvene

pyvene: A Library for Understanding and Improving PyTorch Models via Interventions

https://github.com/franknb/Self-attention-DCGAN

Non-local Neural Networks

https://github.com/freddieee/pn_6d_single

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

https://github.com/freedombenLiu/vid2vid

Video-to-Video Synthesis

https://github.com/frgfm/torch-cam

Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks

https://github.com/frostinassiky/bsp

Boundary-sensitive Pre-training for Temporal Localization in Videos

https://github.com/ftdlyc/pointnet_pytorch

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

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