Who Cited It

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Networks

2014 · 3,197 citations · 9 from inside this corpus

Maxime Oquab low, Léon Bottou, Ivan Laptev, Josef Šivic

The source holds an abstract for this work, but its best open-access copy is under no open licence, which does not permit us to republish the text. Read it at the source below.

Learning and Transferring Mid-level Image Representations Using Convolutional Neural Netw… (2014)Learning and Transferring Mid…Learning representations by back-propagating errors (1986)Learning representations by b…A Survey on Transfer Learning (2009)A Survey on Transfer LearningNatural Language Processing (almost) from Scratch (2011)Natural Language Processing (…Natural Language Processing (almost) from Scratch (2011)Natural Language Processing (…DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition (2013)DeCAF: A Deep Convolutional A…Adapting Visual Category Models to New Domains (2010)Adapting Visual Category Mode…DeCAF: A Deep Convolutional Activation Feature for Generic Visual\n Recognition (2013)DeCAF: A Deep Convolutional A…Deep learning in neural networks: An overview (2014)Deep learning in neural netwo…Domain-Adversarial Training of Neural Networks (2017)Domain-Adversarial Training o…A survey of transfer learning (2016)A survey of transfer learningExploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer (2019)Exploring the Limits of Trans…Bootstrap your own latent: A new approach to self-supervised Learning (2020)Bootstrap your own latent: A …A Survey on Deep Transfer Learning (2018)A Survey on Deep Transfer Lea…Unsupervised Domain Adaptation by Backpropagation (2014)Unsupervised Domain Adaptatio…Deep Hashing Network for Unsupervised Domain Adaptation (2017)Deep Hashing Network for Unsu…Over-the-Air Deep Learning Based Radio Signal Classification (2018)Over-the-Air Deep Learning Ba…
16 of 16 neighbouring works in this corpus. Blue is what this paper cites; orange is what cites it, and a dashed line is one neighbour citing another. Only the largest labels are drawn — every node carries its full title on hover.
this paper works it cites works citing it node size = global citations · hover for the full title

What this paper cites, inside the corpus

PaperYearCited
Learning representations by back-propagating errors198631,688
A Survey on Transfer Learning200923,670
Natural Language Processing (almost) from Scratch20115,174
Natural Language Processing (almost) from Scratch20113,991
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition20133,564
Adapting Visual Category Models to New Domains20102,873
DeCAF: A Deep Convolutional Activation Feature for Generic Visual\n Recognition20131,803

What cites it, inside the corpus

PaperYearCited
Deep learning in neural networks: An overview201418,236
Domain-Adversarial Training of Neural Networks20177,702
A survey of transfer learning20166,290
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer20193,698
Bootstrap your own latent: A new approach to self-supervised Learning20203,447
A Survey on Deep Transfer Learning20182,907
Unsupervised Domain Adaptation by Backpropagation20142,608
Deep Hashing Network for Unsupervised Domain Adaptation20172,081
Over-the-Air Deep Learning Based Radio Signal Classification20181,604

Links

DOI · OpenAlex record

Topics

Domain Adaptation and Few-Shot LearningComputer Science
Advanced Neural Network ApplicationsComputer Science
Multimodal Machine Learning ApplicationsComputer Science

Is this record sound?

complete

Nothing in this record contradicts itself and no field we check is missing.

  • supports4 author record(s) attached.
  • supports69 reference(s) recorded.
  • supportsThe DOI's year agrees with the publication year.
  • supportsA title is present.

Provenance

Everything above was read from one stored OpenAlex payload, fetched 2026-09-04T03:58:47+00:00.

sha256 a08467ae9504f237…