Papers with Code 아카이브

General Classification

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전체 기법전체 데이터셋

하위 태스크

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평가표

11

보존된 평가 기록

19

코드

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

https://github.com/zihangJiang/TokenLabeling

공식 코드

All Tokens Matter: Token Labeling for Training Better Vision Transformers

https://github.com/zijian-hu/SimPLE

공식 코드

SimPLE: Similar Pseudo Label Exploitation for Semi-Supervised Classification

https://github.com/zizi1532/BasisCustomize

공식 코드

Categorical Metadata Representation for Customized Text Classification

https://github.com/zj10/ND-Adam

공식 코드

Normalized Direction-preserving Adam

https://github.com/zjhuang22/maskscoring_rcnn

공식 코드

Mask Scoring R-CNN

https://github.com/zju-vipa/KamalEngine

공식 코드

Amalgamating Knowledge towards Comprehensive Classification

https://github.com/zjunlp/deepke

공식 코드

The Devil is the Classifier: Investigating Long Tail Relation Classification with Decoupling Analysis

https://github.com/zysszy/preferential-labeling

공식 코드

Generalized Equivariance and Preferential Labeling for GNN Node Classification

https://github.com/zzbn12345/WHOSe_Heritage

공식 코드

WHOSe Heritage: Classification of UNESCO World Heritage "Outstanding Universal Value" Documents with Soft Labels

https://github.com/zzd1992/Adversarial-Defense-by-Suppressing-High-Frequencies

공식 코드

Adversarial Defense by Suppressing High-frequency Components

https://github.com/zzstefan/BrainNetClass

공식 코드

Brain Network Construction and Classification Toolbox (BrainNetClass)

https://gitlab.com/Queuecumber/jpeg-domain-resnet

공식 코드

Deep Residual Learning in the JPEG Transform Domain

https://gitlab.com/artelabsuper/hdlv3

공식 코드

Learn Class Hierarchy using Convolutional Neural Networks

https://gitlab.com/dbdmg/dac

공식 코드

Scaling associative classification for very large datasets

https://gitlab.com/hararticles/orthovar

공식 코드

On orthogonal projections for dimension reduction and applications in augmented target loss functions for learning problems

https://gitlab.com/luoyan/gsoftmax

공식 코드

$\mathcal{G}$-softmax: Improving Intra-class Compactness and Inter-class Separability of Features

https://gitlab.com/mborisyak/ope

공식 코드

$(1 + \varepsilon)$-class Classification: an Anomaly Detection Method for Highly Imbalanced or Incomplete Data Sets

https://gitlab.com/mipl/graph-learning-network

공식 코드

Graph Learning Network: A Structure Learning Algorithm

https://gitlab.com/miquelca32/features

공식 코드

Sickle-cell disease diagnosis support selecting the most appropriate machinelearning method: Towards a general and interpretable approach for cellmorphology analysis from microscopy images

https://gitlab.com/nbatfai/smnist

공식 코드

On the notion of number in humans and machines

https://gitlab.com/peroyose/radial_prediction_layers

공식 코드

Radial Prediction Layer

https://gitlab.com/thomio/2srelu

공식 코드

Image classification in frequency domain with 2SReLU: a second harmonics superposition activation function

https://gitlab.com/wxwilcke/mrgcn

공식 코드

End-to-End Entity Classification on Multimodal Knowledge Graphs

https://gitlab.com/xusong19960424/global_cnn

공식 코드

From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information

https://gitlab.version.fz-juelich.de/cavallaro1/svm_quantum-annealer

공식 코드

Support vector machines on the D-Wave quantum annealer

https://worksheets.codalab.org/worksheets/0x2ae89944846444539c2d08a0b7ff3f6f

공식 코드

On-the-Job Learning with Bayesian Decision Theory

https://worksheets.codalab.org/worksheets/0x55ed347e3beb4fa6971d3a226e48fc92

공식 코드

Shaping Visual Representations with Language for Few-shot Classification

https://worksheets.codalab.org/worksheets/0x7b8f6fbc6b5c49c18ac7ca94aafaa1a7

공식 코드

Network In Network

https://worksheets.codalab.org/worksheets/0x822ba2f9005f49f08755a84443c76456

공식 코드

Defending against Whitebox Adversarial Attacks via Randomized Discretization

https://worksheets.codalab.org/worksheets/0x900e7e41deaa4ec5b2fe41dc50594548

공식 코드

Training Classifiers with Natural Language Explanations

https://worksheets.codalab.org/worksheets/0x978bd1ba6b284678994c067f9c9bdb78

공식 코드

Derivative free optimization via repeated classification

https://worksheets.codalab.org/worksheets/0xbcd424d2bf544c4786efcc0063759b1a

공식 코드

Going Deeper with Convolutions

https://bitbucket.org/taoyds/nbsvm_pos

Self-Adaptive Hierarchical Sentence Model

https://gitee.com/gai-shaoyan/mind3d

Point Transformer

https://github.com/0bserver07/Keras-SegNet-Basic

SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation

https://github.com/0jason000/GoogleNet

Going Deeper with Convolutions

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