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2D Classification

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코드

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

https://github.com/Pandade1997/tera_asvproof

Defense for Black-box Attacks on Anti-spoofing Models by Self-Supervised Learning

https://github.com/Pangoraw/GeoSSL

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/PaperCodeReview/BarlowTwins-TF

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/PaperCodeReview/PixPro-TF

Propagate Yourself: Exploring Pixel-Level Consistency for Unsupervised Visual Representation Learning

https://github.com/Papirapi/Few_shot-learning-for-Object-Detection

Few-shot Object Detection via Feature Reweighting

https://github.com/ParadoxicalNerd/Style-Transfer-Implementation

A Neural Algorithm of Artistic Style

https://github.com/Param-Raval/ipro

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/Param-Raval/shadow-sight

Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal

https://github.com/PatriciaRodrigues1994/Satellite-Image-Segmentation

U-Net: Convolutional Networks for Biomedical Image Segmentation

https://github.com/PatrickHua/SimpleMAE

Masked Autoencoders Are Scalable Vision Learners

https://github.com/PaulEmmanuelSotir/DeepCV

Pyramidal Convolution: Rethinking Convolutional Neural Networks for Visual Recognition

https://github.com/PaulGitt/ShuffleNetV2-tensorflow

ShuffleNet V2: Practical Guidelines for Efficient CNN Architecture Design

https://github.com/PaulZhangIsing/CaffeSSD-opencv4

SSD: Single Shot MultiBox Detector

https://github.com/Pavan-pk/ArtGan_Realism

Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

https://github.com/Pavyel/Novelty-Detection

TAP-DLND 1.0 : A Corpus for Document Level Novelty Detection

https://github.com/Pchank/caffe-sal

Amulet: Aggregating Multi-level Convolutional Features for Salient Object Detection

https://github.com/Pea-Shooter/ADNet

EDVR: Video Restoration with Enhanced Deformable Convolutional Networks

https://github.com/Pedestrian1671022/darknet

YOLOv4: Optimal Speed and Accuracy of Object Detection

https://github.com/PenK1nG/DroneMOT

DroneMOT: Drone-based Multi-Object Tracking Considering Detection Difficulties and Simultaneous Moving of Drones and Objects

https://github.com/PengchengAi/tf-faster-rcnn-pcai

Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks

https://github.com/Pengchengzhi/Yolov3_Own-dataset

YOLOv3: An Incremental Improvement

https://github.com/Pengxiao-Wang/Typeface-and-Font-Style-Transfer

Typeface Completion with Generative Adversarial Networks

https://github.com/Pepitaw/darknet

YOLOv3: An Incremental Improvement

https://github.com/PericlesPet/darknet-demo

YOLO9000: Better, Faster, Stronger

https://github.com/PeterTKovacs/zold137

RetinaMask: Learning to predict masks improves state-of-the-art single-shot detection for free

https://github.com/PetersonSilvadeJesus/yolov3-data-images

YOLOv3: An Incremental Improvement

https://github.com/PetiteBeast/pytorch_style_transfer

A Neural Algorithm of Artistic Style

https://github.com/PetruninAlex/Sketch2Real

Perceptual Losses for Real-Time Style Transfer and Super-Resolution

https://github.com/PettyDevil/caffe_ssd

SSD: Single Shot MultiBox Detector

https://github.com/PhanTom2003/PSPnet

Pyramid Scene Parsing Network

https://github.com/PhanTom2003/SSD

SSD: Single Shot MultiBox Detector

https://github.com/PierreSenellart/theoremkb

Modular Multimodal Machine Learning for Extraction of Theorems and Proofs in Long Scientific Documents (Extended Version)

https://github.com/PierrickPochelu/word_tree_label

YOLO9000: Better, Faster, Stronger

https://github.com/Ping-C/CertifiedObjectDetection

Detection as Regression: Certified Object Detection by Median Smoothing

https://github.com/PingoLH/CenterNet-HarDNet

Objects as Points

https://github.com/PingoLH/FCHarDNet

HarDNet: A Low Memory Traffic Network

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