Papers with Code 아카이브

Binary Classification

태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.

전체 기법전체 데이터셋

하위 태스크

6

평가표

14

보존된 평가 기록

14

코드

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

https://github.com/inejc/paragraph-vectors

Notes on Noise Contrastive Estimation and Negative Sampling

https://github.com/jaingaurav3/GAN-Hacks

Sampling Generative Networks

https://github.com/jdromano2/tpot-nn

Is deep learning necessary for simple classification tasks?

https://github.com/jjgrimm/FairnessReferenceSheet

A Reductions Approach to Fair Classification

https://github.com/josh-ashkinaze/Normalized-Google-Distance

The Google Similarity Distance

https://github.com/jpwahle/emnlp24-prompt-paraphrase

Paraphrase Types for Generation and Detection

https://github.com/jpwahle/prompt-paraphrase

Paraphrase Types for Generation and Detection

https://github.com/jrzech/reproduce-chexnet

The Marginal Value of Adaptive Gradient Methods in Machine Learning

https://github.com/junwenkwan/human-robot-handover

Gesture Recognition for Initiating Human-to-Robot Handovers

https://github.com/junwenkwan/junwenkwan

Gesture Recognition for Initiating Human-to-Robot Handovers

https://github.com/kbatsos/real-time-stereo

Stereo Matching by Training a Convolutional Neural Network to Compare Image Patches

https://github.com/kevinscaria/feasibilityqa

"John is 50 years old, can his son be 65?" Evaluating NLP Models' Understanding of Feasibility

https://github.com/kexinhuang12345/DeepPurpose

DeepDTA: Deep Drug-Target Binding Affinity Prediction

https://github.com/kingcheng2000/GAN

Sampling Generative Networks

https://github.com/learn-ensemble/PY-SUMMA

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

https://github.com/learn-ensemble/R-SUMMA

Unsupervised Evaluation and Weighted Aggregation of Ranked Predictions

https://github.com/libofang/DV-ngram

Ensemble of Generative and Discriminative Techniques for Sentiment Analysis of Movie Reviews

https://github.com/linxi159/GAN-training-tricks

Sampling Generative Networks

https://github.com/linxi159/Tips-and-tricks-to-train-GANs

Sampling Generative Networks

https://github.com/lions-epfl/lipslev

RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion

https://github.com/ltiao/bore

BORE: Bayesian Optimization by Density-Ratio Estimation

https://github.com/mailong25/vietnamese-speech-recognition

wav2vec: Unsupervised Pre-training for Speech Recognition

https://github.com/marcojira/fls

How Faithful is your Synthetic Data? Sample-level Metrics for Evaluating and Auditing Generative Models

https://github.com/mattwerenski/adversarial-ot

The Multimarginal Optimal Transport Formulation of Adversarial Multiclass Classification

https://github.com/mauriceqch/pcc_geo_cnn_samples

Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression

https://github.com/mbzuai-nlp/COLING-2025-Workshop-on-MGT-Detection-Task1

SemEval-2024 Task 8: Multidomain, Multimodel and Multilingual Machine-Generated Text Detection

https://github.com/michael13162/DoodleGAN

Sampling Generative Networks

https://github.com/milliondegree/semantic-segmentation-tensorflow

Brain Tumor Segmentation Based on Refined Fully Convolutional Neural Networks with A Hierarchical Dice Loss

https://github.com/mitchfwx/ISA480

Deep Transfer Learning for Static Malware Classification

https://github.com/mmasden/canonicalpoly

On transversality of bent hyperplane arrangements and the topological expressiveness of ReLU neural networks

https://github.com/mshehrozsajjad/Age-Classification

Rank consistent ordinal regression for neural networks with application to age estimation

https://github.com/mwydmuch/macro-measures-in-xmlc

Consistent algorithms for multi-label classification with macro-at-$k$ metrics

https://github.com/neilrs123/Lexical-Complexity-Prediction

CompLex: A New Corpus for Lexical Complexity Prediction from Likert Scale Data

https://github.com/nii-yamagishilab/ClassNSeg

Multi-task Learning For Detecting and Segmenting Manipulated Facial Images and Videos

https://github.com/nyukat/mammography_metarepository

Deep Learning to Improve Breast Cancer Early Detection on Screening Mammography

https://github.com/p-lambda/verified_calibration

Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration

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