Papers with Code 아카이브

2D Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

27

평가표

0

보존된 평가 기록

0

코드

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

https://github.com/KostadinovShalon/oln-mask

Learning Open-World Object Proposals without Learning to Classify

https://github.com/Kprerak-iisc/data-science

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

https://github.com/Krupal09/MaskRCNN-Demo

Mask R-CNN

https://github.com/Kumara-Kaushik/proba_v_challenge

A Neural Algorithm of Artistic Style

https://github.com/KunZhou9646/controllable_evc_code

Seen and Unseen emotional style transfer for voice conversion with a new emotional speech dataset

https://github.com/Kungbohan/EECSMed

MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications

https://github.com/Kunhao-Liu/StyleGaussian

StyleGaussian: Instant 3D Style Transfer with Gaussian Splatting

https://github.com/KushaalShroff/Signature-and-Annotation-detection

Fast R-CNN

https://github.com/KwaiVGI/LivePortrait

Ditto: Motion-Space Diffusion for Controllable Realtime Talking Head Synthesis

https://github.com/KwangHoonAn/Quantizations

Data-Free Quantization Through Weight Equalization and Bias Correction

https://github.com/KyanChen/RSMamba

Mamba: Linear-Time Sequence Modeling with Selective State Spaces

https://github.com/KyeongmoonKim/sb

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

https://github.com/KylianvG/Embetter

Man is to Computer Programmer as Woman is to Homemaker? Debiasing Word Embeddings

https://github.com/Kyubyong/expressive_tacotron

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

https://github.com/L-Reichardt/Cylinder3D-updated-CUDA

Cylinder3D: An Effective 3D Framework for Driving-scene LiDAR Semantic Segmentation

https://github.com/LEGO999/YOLOV3-TF2

YOLOv3: An Incremental Improvement

https://github.com/LEOCUIZHIHAO/kpmask

MMDetection: Open MMLab Detection Toolbox and Benchmark

https://github.com/LIU1514/Yolov3-

YOLO9000: Better, Faster, Stronger

https://github.com/LKLQQ/CBAM

CBAM: Convolutional Block Attention Module

https://github.com/LKLQQ/HireMLP

Hire-MLP: Vision MLP via Hierarchical Rearrangement

https://github.com/LKLQQ/LEO

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/LKLQQ/MIMO-UNet

Rethinking Coarse-to-Fine Approach in Single Image Deblurring

https://github.com/LKLQQ/detr

End-to-End Object Detection with Transformers

https://github.com/LKLQQ/pointpillars

PointPillars: Fast Encoders for Object Detection from Point Clouds

https://github.com/LKLQQ/ssc_resnet50

Deep Residual Learning for Image Recognition

https://github.com/LKLQQ/tracktor

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

https://github.com/LLNL/pysaber

SABER: A Systems Approach to Blur Estimation and Reduction in X-ray Imaging

https://github.com/LONG-9621/Extract_Point_3D

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/LONG-9621/PointNet

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

https://github.com/LONG-9621/PointNet-

Frustum PointNets for 3D Object Detection from RGB-D Data

https://github.com/LONG-9621/VoteNet

Deep Hough Voting for 3D Object Detection in Point Clouds

https://github.com/LRacoci/permutation-graphml

Non-local Neural Networks

https://github.com/LTH14/FSKD

Few Sample Knowledge Distillation for Efficient Network Compression

https://github.com/LTTM/IL-SemSegm

Incremental Learning Techniques for Semantic Segmentation

https://github.com/Lakerszjb/CentereNet-C

Objects as Points

https://github.com/LanqingLi/Chromosome

SSD: Single Shot MultiBox Detector

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