Papers with Code 아카이브
Image Classification
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Image Classification is a fundamental task in vision recognition that aims to understand and categorize an image as a whole under a specific label. Unlike object detection, which involves classification and location of multiple objects within an image, image classification typically pertains to single-object images. When the classification becomes highly detailed or reaches instance-level, it is often referred to as image retrieval, which also involves finding similar images in a large database.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hy18284/mixdiff
Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP
https://github.com/hyang0129/foodclassapp
Regularizing Neural Networks by Penalizing Confident Output Distributions
https://github.com/hycis/TensorGraph
Very Deep Convolutional Networks for Large-Scale Image Recognition
https://github.com/hyeon95y/sparselinear
https://github.com/hysts/pytorch_D-RISE
RISE: Randomized Input Sampling for Explanation of Black-box Models
https://github.com/hysts/pytorch_cutmix
CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features
https://github.com/hysts/pytorch_resnet
https://github.com/hysts/pytorch_resnet_preact
https://github.com/hysts/pytorch_wrn
https://github.com/hyunwoongko/resnext-parallel
Aggregated Residual Transformations for Deep Neural Networks
https://github.com/hz-ants/arc-robot-vision
https://github.com/hz2538/iot_JennyGo
https://github.com/i3p9/deepfake-detection-with-xception
Xception: Deep Learning with Depthwise Separable Convolutions
https://github.com/iArunava/DCGAN
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/iArunava/ResNet
https://github.com/iacolippo/octconv-pytorch
Drop an Octave: Reducing Spatial Redundancy in Convolutional Neural Networks with Octave Convolution
https://github.com/iamarjunchandra/LayoutLM-Form-Understanding---Sequence-Labeling
https://github.com/iamhankai/ghostnet.pytorch
https://github.com/iamilyasedunov/key_word_spotting
EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks
https://github.com/iamkucuk/DCGAN-Face-Generation
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
https://github.com/iamsingh/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/iankuoli/DFNet_TF2
An Intriguing Failing of Convolutional Neural Networks and the CoordConv Solution
https://github.com/iantimmis/DropBlock-Keras-Implementation
DropBlock: A regularization method for convolutional networks
https://github.com/ibabbar/Traffic-Sign-Classifier
Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift
https://github.com/iberganzo/darknet
CSPNet: A New Backbone that can Enhance Learning Capability of CNN
https://github.com/ibm/vira-intent-discovery
https://github.com/icanswim/cxr
https://github.com/ice-melt/csdn-w9
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/iclavera/meta-mb
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/icoz69/DeepEMD
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning
https://github.com/icrto/xML
Evaluating the visualization of what a Deep Neural Network has learned
https://github.com/idealo/imagededup
MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications
https://github.com/idiap/fullgrad-saliency
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
https://github.com/ido90/roml-maml
Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks
https://github.com/idobronstein/my_WRN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.