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General Classification

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코드

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

https://github.com/martinambition/IntentClassificationBenchmark

Subword Semantic Hashing for Intent Classification on Small Datasets

https://github.com/martinepalazzo/biomed_dim_red_thesis

Latent regularization for feature selection using kernel methods in tumor classification

https://github.com/martinsbruveris/tensorflow-image-models

ResMLP: Feedforward networks for image classification with data-efficient training

https://github.com/marwan-elsafty/modulation-classification

Convolutional Radio Modulation Recognition Networks

https://github.com/masakicktashiro/rgcn_pytorch_implementation

Modeling Relational Data with Graph Convolutional Networks

https://github.com/mashaan14/ADDA

Adversarial Discriminative Domain Adaptation

https://github.com/mashaan14/ADDA-toy

Adversarial Discriminative Domain Adaptation

https://github.com/mashaan14/DANN-toy

Domain-Adversarial Training of Neural Networks

https://github.com/masukislam/DS8008_NLP

Pairwise Multi-Class Document Classification for Semantic Relations between Wikipedia Articles

https://github.com/matej-ulicny/harmonic-networks

Harmonic Networks: Integrating Spectral Information into CNNs

https://github.com/mathieuorhan/darknet

YOLO9000: Better, Faster, Stronger

https://github.com/mathkey/mssnet

Multi-Label Classification with Label Graph Superimposing

https://github.com/mathworks/Grad-CAM-for-AlexNet-to-explain-the-reason-of-classification

Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization

https://github.com/matkowski-voy/HandsGender

Gender and Ethnicity Classification based on Palmprint and Palmar Hand Images from Uncontrolled Environment

https://github.com/matlab-deep-learning/Explore-Deep-Network-Explainability-Using-an-App

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/mattbev/6.867-final-project

Exploiting Defenses against GAN-Based Feature Inference Attacks in Federated Learning

https://github.com/mattdeitke/cvpr-buzz

VirTex: Learning Visual Representations from Textual Annotations

https://github.com/mattemartini/explainable-and-trustworthy-ai-project

Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps

https://github.com/matthklein/equalized_odds_with_perturbed_attribute

Equalized odds postprocessing under imperfect group information

https://github.com/maudzung/TTNet-Real-time-Analysis-System-for-Table-Tennis-Pytorch

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/maudzung/TTNet-Realtime-for-Table-Tennis-Pytorch

Multi-Task Learning Using Uncertainty to Weigh Losses for Scene Geometry and Semantics

https://github.com/mavenlin/cuda-convnet

Maxout Networks

https://github.com/maxencenoble/Differential-Privacy-for-Heterogeneous-Federated-Learning

Measuring the Effects of Non-Identical Data Distribution for Federated Visual Classification

https://github.com/maxstrobel/HCN-PrototypeLoss-PyTorch

Prototypical Networks for Few-shot Learning

https://github.com/maxwellyin/mabr

Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting

https://github.com/mayankk6196/darts-python

DARTS: Differentiable Architecture Search

https://github.com/mayer79/missRanger

ranger: A Fast Implementation of Random Forests for High Dimensional Data in C++ and R

https://github.com/maysa96/music_tagger_mine2

Convolutional Recurrent Neural Networks for Music Classification

https://github.com/mayurikumari047/Aspect-Based-Sentiment-Analysis

Aspect Level Sentiment Classification with Deep Memory Network

https://github.com/mayurikumari047/Aspect-Based-Sentiment-Analysis-Multiple-Models

Aspect Level Sentiment Classification with Deep Memory Network

https://github.com/mazc2121/boolean-nets

XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

https://github.com/mb010/equivariantselfattention

Attention-gating for improved radio galaxy classification

https://github.com/mbakler/Tcav_pytorch_implementation

Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV)

https://github.com/mbilalzafar/fair-classification

Fairness Constraints: Mechanisms for Fair Classification

https://github.com/mbonto/fewshot_generalization

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

https://github.com/mbonto/fewshot_neuroimaging_classification

SimpleShot: Revisiting Nearest-Neighbor Classification for Few-Shot Learning

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