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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/levyben/DeepSuperLearner

Deep Super Learner: A Deep Ensemble for Classification Problems

https://github.com/lewj85/e2cnn_experiments

Steerable CNNs

https://github.com/lgalke/vec4ir

All-but-the-Top: Simple and Effective Postprocessing for Word Representations

https://github.com/lgcollins/tr-maml

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

https://github.com/lgerrets/asait18-tagging

Unsupervised Feature Learning Based on Deep Models for Environmental Audio Tagging

https://github.com/li-yibing/oceannet

YOLO9000: Better, Faster, Stronger

https://github.com/liamcli/darts

DARTS: Differentiable Architecture Search

https://github.com/liangbright/OOD_Attack_NN

An Algorithm for Out-Of-Distribution Attack to Neural Network Encoder

https://github.com/liaoq/Generalization-Puzzles-in-Deep-Networks

A Surprising Linear Relationship Predicts Test Performance in Deep Networks

https://github.com/libffcv/ffcv-imagenet

Budgeted Training: Rethinking Deep Neural Network Training Under Resource Constraints

https://github.com/libreai/neural-painters-x

Very Deep Convolutional Networks for Large-Scale Image Recognition

https://github.com/lif31up/model-agnostic-meta-learning

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

https://github.com/lif31up/prototypical-network

Prototypical Networks for Few-shot Learning

https://github.com/lifebeyondexpectations/eccv22-pointmixer

Point Transformer

https://github.com/light-curve/light-curve

Deep-Learnt Classification of Light Curves

https://github.com/lightaime/sgas

SGAS: Sequential Greedy Architecture Search

https://github.com/lightly-ai/lightly

Barlow Twins: Self-Supervised Learning via Redundancy Reduction

https://github.com/lightning-uq-box/lightning-uq-box

On Feature Collapse and Deep Kernel Learning for Single Forward Pass Uncertainty

https://github.com/lim0606/caffe-googlenet-bn

Going Deeper with Convolutions

https://github.com/limchaos/likelihood-ood

The iNaturalist Species Classification and Detection Dataset

https://github.com/linghduoduo/NLP

Deep Learning applied to NLP

https://github.com/lingtengqiu/Yolo_Nano

Bag of Freebies for Training Object Detection Neural Networks

https://github.com/linguishi/chinese_sentiment

Convolutional Neural Networks for Sentence Classification

https://github.com/linkfluence/fastText4j

Bag of Tricks for Efficient Text Classification

https://github.com/linmengsysu/IsoNN

IsoNN: Isomorphic Neural Network for Graph Representation Learning and Classification

https://github.com/linrongc/youtube-8m

NeXtVLAD: An Efficient Neural Network to Aggregate Frame-level Features for Large-scale Video Classification

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

Sampling Generative Networks

https://github.com/linxi159/KittiSeg

MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving

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

Sampling Generative Networks

https://github.com/linyang-zhh/FQ-ViT

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

https://github.com/lipingcoding/pygcn

Semi-Supervised Classification with Graph Convolutional Networks

https://github.com/lippman1125/darts_notes

DARTS: Differentiable Architecture Search

https://github.com/liqichen6688/baycnn

Weight Uncertainty in Neural Networks

https://github.com/liqichen6688/duo-attention

A Structured Self-attentive Sentence Embedding

https://github.com/liquidSVM/liquidSVM

liquidSVM: A Fast and Versatile SVM package

https://github.com/lironber/GOAD

Classification-Based Anomaly Detection for General Data

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