Papers with Code 아카이브

Small Data Image Classification

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하위 태스크: 0
평가표: 12
보존된 평가 기록: 47

Supervised image classification with tens to hundreds of labeled training examples.

코드

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

https://github.com/ostadabbas/ScanAvaGenerationToolkit

공식 코드

A Semi-Supervised Data Augmentation Approach using 3D Graphical Engines

https://github.com/parshakova/GAMS-for-Data-Efficient-Learning

공식 코드

Global Autoregressive Models for Data-Efficient Sequence Learning

https://github.com/pfjaeger/medicaldetectiontoolkit

공식 코드

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

https://github.com/rcgc/chihuahua-muffin

공식 코드

Deep Learning Approach for Very Similar Objects Recognition Application on Chihuahua and Muffin Problem

https://github.com/rezkaaufar/fast-and-flexible

공식 코드

The Fast and the Flexible: training neural networks to learn to follow instructions from small data

https://github.com/varunmannam/Papers_with_Code/tree/master/VAE_project

공식 코드

Performance Analysis of Semi-supervised Learning in the Small-data Regime using VAEs

https://github.com/zhang64-llnl/Mix-n-Match-Calibration

공식 코드

Mix-n-Match: Ensemble and Compositional Methods for Uncertainty Calibration in Deep Learning

https://github.com/AmirMoMo/COVID-19

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

https://github.com/DTUComputeStatisticsAndDataAnalysis/Weight-Share

Deep learning for Chemometric and non-translational data

https://github.com/Fw9wef/DetectorGAN

Generative Modeling for Small-Data Object Detection

https://github.com/MIC-DKFZ/DetectionAndRegression

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

https://github.com/MIC-DKFZ/RegRCNN

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

https://github.com/MehdiChelh/CuMF_SGD

CuMF_SGD: Fast and Scalable Matrix Factorization

https://github.com/MindSpore-scientific-2/code-10/tree/main/Translation-Invariant

On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location

https://github.com/Mjrovai/covid19Xray

Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset

https://github.com/OscarPellicer/prostate_lesion_detection

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

https://github.com/PhIMaL/DeePyMoD_torch

DeepMoD: Deep learning for Model Discovery in noisy data

https://github.com/SebastianMacaluso/ClusterTrellis

Data Structures & Algorithms for Exact Inference in Hierarchical Clustering

https://github.com/Tnorm/SVM_cookietest

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

https://github.com/YutongWangUMich/UCI_classification

Harnessing the Power of Infinitely Wide Deep Nets on Small-data Tasks

https://github.com/anbai106/mlni

Reproducible evaluation of diffusion MRI features for automatic classification of patients with Alzheimers disease

https://github.com/aredier/monte_carlo_dropout

Bayesian Convolutional Neural Networks with Bernoulli Approximate Variational Inference

https://github.com/bionicdl-sustech/DeepClaw

Rigid-Soft Interactive Learning for Robust Grasping

https://github.com/bionicdl-sustech/DeepClawBenchmark

Rigid-Soft Interactive Learning for Robust Grasping

https://github.com/exploring-curiosity/CovidPredictionML

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

https://github.com/firojalam/multimodal_social_media

Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response

https://github.com/gaur-pankaj/Covid19-Detection

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

https://github.com/ilmimris/bangkit-bgd2a-virus-covid-vs-noncovid

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

https://github.com/imanpalsingh/COVID-19-Detection--from-X-rays

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

https://github.com/imanpalsingh/COVID-19-Diagnosis-using-Convolutional-and-Generative-models

Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small data

https://github.com/ivanwilliammd/I3DR-Net-Transfer-Learning

Retina U-Net: Embarrassingly Simple Exploitation of Segmentation Supervision for Medical Object Detection

https://github.com/jindongwang/transferlearning

Learning What and Where to Transfer

https://github.com/junwoopark92/L2T_ww

Learning What and Where to Transfer

https://github.com/kggreene/kggreene.github.io

An Infinite Parade of Giraffes: Expressive Augmentation and Complexity Layers for Cartoon Drawing

https://github.com/pwc-1/Paper-9/tree/main/7/Translation-Invariant/model

On Translation Invariance in CNNs: Convolutional Layers can Exploit Absolute Spatial Location

https://github.com/qin67/Multistep-Time-Series-DO-Case

Comparison of Deep learning models on time series forecasting : a case study of Dissolved Oxygen Prediction

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