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YFCC100M HybridNet fc6 Deep Features for Content-Based Image Retrieval

Giuseppe Amato, Fabrizio Falchi, Claudio Gennaro, Fausto Rabitti
2016 Proceedings of the 2016 ACM Workshop on Multimedia COMMONS - MMCommons '16  
This set of results is ground truth for evaluating Content-Based Image Retrieval (CBIR) systems that use approximate similarity search methods for efficient and scalable indexing.  ...  This paper presents a corpus of deep features extracted from the YFCC100M images considering the fc6 hidden layer activation of the HybridNet deep convolutional neural network.  ...  Moreover, we evaluated different implementation strategies to index deep features for large-scale CBIR datasets as YFCC100M.  ... 
doi:10.1145/2983554.2983557 fatcat:ayqkkrvufbgmxoicy35iogneju

Towards Demystifying Serverless Machine Learning Training [article]

Jiawei Jiang, Shaoduo Gan, Yue Liu, Fanlin Wang, Gustavo Alonso, Ana Klimovic, Ankit Singla, Wentao Wu, Ce Zhang
2021 pre-print
Several systems exist for training large-scale ML models on top of serverless infrastructures (e.g., AWS Lambda) but with inconclusive results in terms of their performance and relative advantage over  ...  We present experimental results using LambdaML, and further develop an analytic model to capture cost/performance tradeoffs that must be considered when opting for a serverless infrastructure.  ...  CONCLUSION We conducted a systematic study regarding the tradeoff between FaaS-based and IaaS-based systems for training ML models.  ... 
doi:10.1145/3448016.3459240 arXiv:2105.07806v1 fatcat:zfkakyyfhraj3hnzgyxykl7yfq