Papers with Code 아카이브
Classification
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Classification is the task of categorizing a set of data into predefined classes or groups. The aim of classification is to train a model to correctly predict the class or group of new, unseen data. The model is trained on a labeled dataset where each instance is assigned a class label. The learning algorithm then builds a mapping between the features of the data and the class labels. This mapping is then used to predict the class label of new, unseen data points. The quality of the prediction is usually evaluated using metrics such as accuracy, precision, and recall.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/hc495/Hidden_Calibration
공식 코드Token-based Decision Criteria Are Suboptimal in In-context Learning
https://github.com/hc495/staicc
공식 코드StaICC: Standardized Evaluation for Classification Task in In-context Learning
https://github.com/hccngu/MLADA
공식 코드Meta-Learning Adversarial Domain Adaptation Network for Few-Shot Text Classification
https://github.com/hccngu/meta-sn
공식 코드Meta-Learning Siamese Network for Few-Shot Text Classification
https://github.com/hchen605/ast_inst_cls
공식 코드https://github.com/he-h/HuMob
공식 코드https://github.com/heaventian93/MDAIR
공식 코드Modified Distribution Alignment for Domain Adaptation with Pre-trained Inception ResNet
https://github.com/heimine/nc_mlab
공식 코드Neural Collapse in Multi-label Learning with Pick-all-label Loss
https://github.com/heimine/pnc_dln
공식 코드Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
https://github.com/heimingx/pcm
공식 코드Progressive Class Semantic Matching for Semi-supervised Text Classification
https://github.com/hellowangqian/multi-label-image-classification
공식 코드https://github.com/hemile/iswc-challenge
공식 코드Prompting as Probing: Using Language Models for Knowledge Base Construction
https://github.com/hendraet/handwriting-classification
공식 코드Handwriting Classification for the Analysis of Art-Historical Documents
https://github.com/hennels/CropPreClustering
공식 코드Pre-Clustering Point Clouds of Crop Fields Using Scalable Methods
https://github.com/henrygouk/stochastic-gradient-trees
공식 코드https://github.com/henrylee2570/NoisyAnchor
공식 코드https://github.com/henrymoss/COLING2018
공식 코드Using J-K fold Cross Validation to Reduce Variance When Tuning NLP Models
https://github.com/henulwy/apnt
공식 코드Augmenting Prototype Network with TransMix for Few-shot Hyperspectral Image Classification
https://github.com/henulwy/stbdip
공식 코드Multi-level Relation Learning for Cross-domain Few-shot Hyperspectral Image Classification
https://github.com/heqianpei/diga
공식 코드Distributionally Generative Augmentation for Fair Facial Attribute Classification
https://github.com/hermannkroll/supervisedtextprocessing
공식 코드https://github.com/herolab-uga/matsee
공식 코드https://github.com/hessamb/label-refinery
공식 코드Label Refinery: Improving ImageNet Classification through Label Progression
https://github.com/hezhangsprinter/SROSR
공식 코드https://github.com/hfawaz/bigdata18
공식 코드https://github.com/hfawaz/dl-4-tsc
공식 코드https://github.com/hfawaz/ijcnn19attacks
공식 코드Adversarial Attacks on Deep Neural Networks for Time Series Classification
https://github.com/hfawaz/ijcnn19ensemble
공식 코드Deep Neural Network Ensembles for Time Series Classification
https://github.com/hfawaz/miccai18
공식 코드https://github.com/hhcho/hyplinear
공식 코드https://github.com/hhhhedy/gfss-ekt
공식 코드Enhancing Generalized Few-Shot Semantic Segmentation via Effective Knowledge Transfer
https://github.com/hhi-aml/histobenchmark
공식 코드https://github.com/hiddefok/consequences-of-recourse
공식 코드https://github.com/hihey54/acsac22_spacephish
공식 코드https://github.com/hikaru-nara/dask
공식 코드Domain-Adaptive Text Classification with Structured Knowledge from Unlabeled Data
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.