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In the last couple of years, weakly labeled learning has turned out to be an exciting approach for audio event detection.
“Combining labeled and unlabeled data with co-training,”
Avrim Blum and Tom Mitchell, · 1998
Earlier work this paper cites.
“Clustering with bregman divergences,”
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh, · 2005
Earlier work this paper cites.
“Learning object categories from internet image searches,”
Rob Fergus, Li Fei-Fei, Pietro Perona, and Andrew Zisserman, · 2010
Earlier work this paper cites.
“A survey of multi-view machine learning,”
Shiliang Sun, · 2013
Earlier work this paper cites.
“Learning everything about anything: Webly-supervised visual concept learning,”
Santosh K Divvala, Ali Farhadi, and Carlos Guestrin, · 2014
Earlier work this paper cites.
“Well begun is half done: Generating high-quality seeds for automatic image dataset construction from web,”
Yan Xia, Xudong Cao, Fang Wen, and Jian Sun, · 2014
Earlier work this paper cites.
“Classification in the presence of label noise: a survey,”
Benoît Frénay and Michel Verleysen, · 2014
Earlier work this paper cites.
“Training deep neural networks on noisy labels with bootstrapping,”
Scott Reed, Honglak Lee, Dragomir Anguelov, Christian Szegedy, Dumitru Erhan, and Andrew Rabinovich, · 2014
Earlier work this paper cites.
“Adam: A method for stochastic optimization,”
Diederik P Kingma and Jimmy Ba, · 2014
Earlier work this paper cites.
“Webly supervised learning of convolutional networks,”
Xinlei Chen and Abhinav Gupta, · 2015
Earlier work this paper cites.
“Batch normalization: Accelerating deep network training by reducing internal covariate shift,”
Sergey Ioffe and Christian Szegedy, · 2015
Cited alongside, same era.
“Audio event detection using weakly labeled data,”
Anurag Kumar and Bhiksha Raj, · 2016
Cited alongside, same era.
“Learning to detect concepts from webly-labeled video data,”
Junwei Liang, Lu Jiang, Deyu Meng, and Alexander Hauptmann, · 2016
Cited alongside, same era.
“Training deep neural-networks using a noise adaptation layer,”
Jacob Goldberger and Ehud Ben-Reuven, · 2016
Cited alongside, same era.
“Understanding deep learning requires rethinking generalization,”
Chiyuan Zhang, Samy Bengio, Moritz Hardt, Benjamin Recht, and Oriol Vinyals, · 2016
Cited alongside, same era.
“Discovering sound concepts and acoustic relations in text,”
Anurag Kumar, Bhiksha Raj, and Ndapandula Nakashole, · 2017
Later among the works it cites.
“Decoupling” when to update” from” how to update”,”
Eran Malach and Shai Shalev-Shwartz, · 2017
Later among the works it cites.
“Cnn architectures for large-scale audio classification,”
Shawn Hershey, Sourish Chaudhuri, Daniel PW Ellis, Jort F Gemmeke, Aren Jansen, R Channing Moore, Manoj Plakal, Devin Platt, Rif A Saurous, Bryan Seybold, et al., · 2017
Later among the works it cites.
Acoustic Intelligence in Machines
Anurag Kumar, · 2018
Closest in time.
“Knowledge transfer from weakly labeled audio using convolutional neural network for sound events and scenes,”
Anurag Kumar, M. Khadkevich, and C. Fugen, · 2018
Closest in time.
“Learning to recognize transient sound events using attentional supervision.,”
Szu-Yu Chou, Jyh-Shing Roger Jang, and Yi-Hsuan Yang, · 2018
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“Spda-cnn: Unifying semantic part detection and abstraction for fine-grained recognition,”
Han Zhang, Tao Xu, Mohamed Elhoseiny, Xiaolei Huang, Shaoting Zhang, Ahmed Elgammal, and Dimitris Metaxas, · 2016
Cited alongside, same era.
Yong Xu, Qiuqiang Kong, Qiang Huang, Wenwu Wang, and Mark D Plumbley, · 2017
Cited alongside, same era.
“Audio set: An ontology and human-labeled dataset for audio events,”
Jort F Gemmeke, Daniel PW Ellis, Dylan Freedman, Aren Jansen, Wade Lawrence, R Channing Moore, Manoj Plakal, and Marvin Ritter, · 2017
Cited alongside, same era.
“Freesound datasets: a platform for the creation of open audio datasets,”
Eduardo Fonseca, Jordi Pons Puig, Xavier Favory, Frederic Font Corbera, Dmitry Bogdanov, Andres Ferraro, Sergio Oramas, Alastair Porter, and Xavier Serra, · 2017
Cited alongside, same era.
“Audio event and scene recognition: A unified approach using strongly and weakly labeled data,”
Anurag Kumar and Bhiksha Raj, · 2017
Cited alongside, same era.
Closest in time.
“Adaptive pooling operators for weakly labeled sound event detection,”
Brian McFee, Justin Salamon, and Juan Pablo Bello, · 2018
Closest in time.
“Temporal attentive pooling for acoustic event detection,”
Xugang Lu, Peng Shen, Sheng Li, Yu Tsao, and Hisashi Kawai, · 2018
Closest in time.
“Joint optimization framework for learning with noisy labels,”
Daiki Tanaka, Daiki Ikami, Toshihiko Yamasaki, and Kiyoharu Aizawa, · 2018
Closest in time.
“Co-teaching: robust training deep neural networks with extremely noisy labels,”
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama, · 2018
Closest in time.