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General Classification

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평가표

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보존된 평가 기록

19

코드

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

https://github.com/giannisnik/repset

공식 코드

Rep the Set: Neural Networks for Learning Set Representations

https://github.com/gibranfp/P300-CNNT

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A few filters are enough: Convolutional Neural Network for P300 Detection

https://github.com/gidariss/FeatureLearningRotNet

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Unsupervised Representation Learning by Predicting Image Rotations

https://github.com/gidariss/FewShotWithoutForgetting

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Dynamic Few-Shot Visual Learning without Forgetting

https://github.com/gidariss/wDAE_GNN_FewShot

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Generating Classification Weights with GNN Denoising Autoencoders for Few-Shot Learning

https://github.com/gifford-lab/overinterpretation

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Overinterpretation reveals image classification model pathologies

https://github.com/ginevracoal/BayesianRelevance

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Resilience of Bayesian Layer-Wise Explanations under Adversarial Attacks

https://github.com/gistairc/MUSIC4P3

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Solar Power Plant Detection on Multi-Spectral Satellite Imagery using Weakly-Supervised CNN with Feedback Features and m-PCNN Fusion

https://github.com/giuliosiracusano/deeplearning_crack_classification

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Automatic crack classification by exploiting statistical event descriptors for Deep Learning

https://github.com/gkaramanolakis/clts

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Cross-Lingual Text Classification with Minimal Resources by Transferring a Sparse Teacher

https://github.com/gkioxari/RstarCNN

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Contextual Action Recognition with R*CNN

https://github.com/gknilsen/pydeepdelta

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Epistemic Uncertainty Quantification in Deep Learning Classification by the Delta Method

https://github.com/gmum/classification-optimal-network-depth

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Finding the Optimal Network Depth in Classification Tasks

https://github.com/gokriznastic/HybridSN

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HybridSN: Exploring 3D-2D CNN Feature Hierarchy for Hyperspectral Image Classification

https://github.com/goldblum/AdversarialQuerying

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Adversarially Robust Few-Shot Learning: A Meta-Learning Approach

https://github.com/goldblum/FeatureClustering

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Unraveling Meta-Learning: Understanding Feature Representations for Few-Shot Tasks

https://github.com/gomezzz/MSMatch

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MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labels

https://github.com/google-health/records-research

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Improved Hierarchical Patient Classification with Language Model Pretraining over Clinical Notes

https://github.com/google-research/deep_representation_one_class

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Learning and Evaluating Representations for Deep One-class Classification

https://github.com/google-research/google-research

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Large Margin Deep Networks for Classification

https://github.com/google-research/google-research/tree/master/rce

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Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification

https://github.com/google-research/google-research/tree/master/vatt

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VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text

https://github.com/google-research/leaf-audio

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LEAF: A Learnable Frontend for Audio Classification

https://github.com/google-research/meta-dataset

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Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples

https://github.com/google-research/noisy-fewshot-learning

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Graph convolutional networks for learning with few clean and many noisy labels

https://github.com/google-research/noisystudent

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Self-training with Noisy Student improves ImageNet classification

https://github.com/google-research/scenic

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ViViT: A Video Vision Transformer

https://github.com/google-research/vmf_embeddings

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von Mises-Fisher Loss: An Exploration of Embedding Geometries for Supervised Learning

https://github.com/google/TensorNetwork

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TensorNetwork for Machine Learning

https://github.com/google/uncertainty-baselines

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Exploring the Uncertainty Properties of Neural Networks' Implicit Priors in the Infinite-Width Limit

https://github.com/goschjann/ssltsc

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Deep Semi-Supervised Learning for Time Series Classification

https://github.com/gouwsmeister/bilbowa

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BilBOWA: Fast Bilingual Distributed Representations without Word Alignments

https://github.com/goyalanil/DAMVI

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Diversity-Aware Weighted Majority Vote Classifier for Imbalanced Data

https://github.com/gparisa/eegbciMI

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Comparison of Classification Algorithms Towards Subject-Specific and Subject-Independent BCI

https://github.com/gperezs/StarcNet

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StarcNet: Machine Learning for Star Cluster Identification

https://github.com/gpitsilis/hate-speech

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Detecting Offensive Language in Tweets Using Deep Learning

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