🔗 Who cites «DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition»

170 papers of our library cite it (of the 149,439 whose bibliographies we read) · 21 with a page here, the most cited first · ← back to «DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition»

1. Very Deep Convolutional Networks for Large-Scale Image RecognitionarXiv:1409.1556Karen Simonyan, Andrew Zisserman2014 · 73,393 citations
2. Rich feature hierarchies for accurate object detection and semantic segmentationIEEE Computer Society Conference on Computer Vision and Pattern RecognitionRoss Girshick, Jeff Donahue, Trevor Darrell, et al.2013 · 31,930 citations
3. A Simple Framework for Contrastive Learning of Visual RepresentationsarXiv:2002.05709Ting Chen, Simon Kornblith, Mohammad Norouzi, et al.2020 · 8,000 citations
4. Learning Transferable Architectures for Scalable Image RecognitionIEEE Computer Society Conference on Computer Vision and Pattern RecognitionBarret Zoph, Vijay Vasudevan, Jonathon Shlens, et al.2017 · 6,583 citations
5. Model-Agnostic Meta-Learning for Fast Adaptation of Deep NetworksarXiv:1703.03400Chelsea Finn, Pieter Abbeel, Sergey Levine2017 · 6,549 citations
6. A Broad-Coverage Challenge Corpus for Sentence Understanding through InferenceConference of the North American Chapter of the Association for Computational Linguistics (NAACL)Adina Williams, Nikita Nangia, Samuel R. Bowman2018 · 5,551 citations
7. Caffe: Convolutional Architecture for Fast Feature EmbeddingarXiv:1408.5093Yangqing Jia, Evan Shelhamer, Jeff Donahue, et al.2014 · 4,209 citations
8. Continual Lifelong Learning with Neural Networks: A ReviewarXiv:1802.07569German I. Parisi, Ronald Kemker, Jose L. Part, et al.2018 · 3,224 citations
9. Understanding Black-box Predictions via Influence FunctionsarXiv:1703.04730Pang Wei Koh, Percy Liang2017 · 1,514 citations
10. Matching Networks for One Shot LearningarXiv:1606.04080Oriol Vinyals, Charles Blundell, Timothy Lillicrap, et al.2016 · 1,197 citations
11. VL-BERT: Pre-training of Generic Visual-Linguistic RepresentationsarXiv:1908.08530Weijie Su, Xizhou Zhu, Yue Cao, et al.2019 · 1,066 citations
12. BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply ChainarXiv:1708.06733Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg2017 · 1,024 citations
13. Visualizing and Understanding Convolutional NetworksarXiv:1311.2901Matthew D Zeiler, Rob Fergus2013 · 771 citations
14. Show and Tell: A Neural Image Caption GeneratorarXiv:1411.4555Oriol Vinyals, Alexander Toshev, Samy Bengio, et al.2014 · 729 citations
15. Using millions of emoji occurrences to learn any-domain representations for detecting sentiment, emotion and sarcasmConference on Empirical Methods in Natural Language Processing (EMNLP)Bjarke Felbo, Alan Mislove, Anders Søgaard, et al.2017 · 621 citations
16. Model soups: averaging weights of multiple fine-tuned models improves accuracy without increasing inference timearXiv:2203.05482Mitchell Wortsman, Gabriel Ilharco, Samir Yitzhak Gadre, et al.2022 · 507 citations
17. How Transferable are Neural Networks in NLP Applications?Conference on Empirical Methods in Natural Language Processing (EMNLP)Lili Mou, Zhao Meng, Rui Yan, et al.2016 · 328 citations
18. Multi-task Sequence to Sequence LearningarXiv:1511.06114Minh-Thang Luong, Quoc V. Le, Ilya Sutskever, et al.2015 · 321 citations
19. Socratic Models: Composing Zero-Shot Multimodal Reasoning with LanguagearXiv:2204.00598Andy Zeng, Maria Attarian, Brian Ichter, et al.2022 · 302 citations
20. Scaling Down to Scale Up: A Guide to Parameter-Efficient Fine-TuningarXiv:2303.15647Vladislav Lialin, Vijeta Deshpande, Xiaowei Yao, et al.2023 · 129 citations
21. Finding Neurons in a Haystack: Case Studies with Sparse ProbingarXiv:2305.01610Wes Gurnee, Neel Nanda, Matthew Pauly, et al.2023 · 123 citations

149 more papers of our library cite it; they have no page here yet.

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