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General Classification

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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/bochencaltech/scotopic

공식 코드

Seeing into Darkness: Scotopic Visual Recognition

https://github.com/bohu615/nu_gan

공식 코드

Unsupervised Learning for Cell-level Visual Representation in Histopathology Images with Generative Adversarial Networks

https://github.com/boknilev/asr-repr-analysis

공식 코드

Analyzing Hidden Representations in End-to-End Automatic Speech Recognition Systems

https://github.com/borgr/GEC_UD_divergences

공식 코드

Classifying Syntactic Errors in Learner Language

https://github.com/bothe/EDAs

공식 코드

EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

https://github.com/bothe/dialogue-act-recognition

공식 코드

A Context-based Approach for Dialogue Act Recognition using Simple Recurrent Neural Networks

https://github.com/boun-tabi/RELX

공식 코드

The RELX Dataset and Matching the Multilingual Blanks for Cross-Lingual Relation Classification

https://github.com/bowenc0221/Decoupled-Classification-Refinement

공식 코드

Decoupled Classification Refinement: Hard False Positive Suppression for Object Detection

https://github.com/bpkindo/bayesqart

공식 코드

Bayesian quantile additive regression trees

https://github.com/bplank/ijcnlp2017-customer-feedback

공식 코드

ALL-IN-1: Short Text Classification with One Model for All Languages

https://github.com/brcsomnath/Advanced-GAP

공식 코드

SIMILARnet: Simultaneous Intelligent Localization and Recognition Network

https://github.com/brendenlake/omniglot

공식 코드

The Omniglot challenge: a 3-year progress report

https://github.com/bryan-he/closek

공식 코드

Minimizing Close-k Aggregate Loss Improves Classification

https://github.com/bupt-ai-cz/Hybrid-Model-Enabling-Highly-Efficient-Follicular-Segmentation

공식 코드

Highly Efficient Follicular Segmentation in Thyroid Cytopathological Whole Slide Image

https://github.com/buraksatar/car-detection-model-prediction

공식 코드

Deep Learning Based Vehicle Make-Model Classification

https://github.com/bwallace/rationale-CNN

공식 코드

Rationale-Augmented Convolutional Neural Networks for Text Classification

https://github.com/bzantium/bert-AAD

공식 코드

Knowledge Distillation for BERT Unsupervised Domain Adaptation

https://github.com/c-hofer/graph_filtration_learning

공식 코드

Graph Filtration Learning

https://github.com/cadurosar/benchmark_graphinference

공식 코드

Graph topology inference benchmarks for machine learning

https://github.com/caiqi/sample-weighting-network

공식 코드

Learning a Unified Sample Weighting Network for Object Detection

https://github.com/cambridge-mlg/cnaps

공식 코드

Fast and Flexible Multi-Task Classification Using Conditional Neural Adaptive Processes

https://github.com/cambridgeltl/mling_sdgms

공식 코드

Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification

https://github.com/camcastera/INNA-for-DeepLearning

공식 코드

An Inertial Newton Algorithm for Deep Learning

https://github.com/camcastera/Indian-for-DeepLearning

공식 코드

An Inertial Newton Algorithm for Deep Learning

https://github.com/canonical-capsules/canonical-capsules

공식 코드

Canonical Capsules: Self-Supervised Capsules in Canonical Pose

https://github.com/caojiguo/FunClassifiers

공식 코드

Neural Networks as Functional Classifiers

https://github.com/cardiffnlp/tweeteval

공식 코드

TweetEval: Unified Benchmark and Comparative Evaluation for Tweet Classification

https://github.com/carlesventura/iterative-deep-learning

공식 코드

Iterative Deep Learning for Road Topology Extraction

https://github.com/castorini/hedwig

공식 코드

DocBERT: BERT for Document Classification

https://github.com/catboost/catboost

공식 코드

CatBoost: unbiased boosting with categorical features

https://github.com/cauchyturing/Deconv_SAX

공식 코드

Representation Learning with Deconvolution for Multivariate Time Series Classification and Visualization

https://github.com/cauchyturing/UCR_Time_Series_Classification_Deep_Learning_Baseline

공식 코드

Time Series Classification from Scratch with Deep Neural Networks: A Strong Baseline

https://github.com/cbfinn/maml

공식 코드

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/cbfinn/maml_rl

공식 코드

Model-Agnostic Meta-Learning for Fast Adaptation of Deep Networks

https://github.com/ccc-frankfurt/DP-NAS

공식 코드

Neural Architecture Search of Deep Priors: Towards Continual Learning without Catastrophic Interference

https://github.com/cccorn/AP-loss

공식 코드

Towards Accurate One-Stage Object Detection with AP-Loss

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