Abstract
Weakly labeled data is hard to classify as it does not contain any time tag of the given data making it harder to identify the target to be trained. Previous works have used joint detection classification model using detector and classifier together to identify the presence of events in frames and classify them later. To increase the efficiency of the neural network, several models use very deep models with arbitrary depth but such model plateau very quickly and take long time to train. We propose skip layer connections in deep neural networks so rather than passing previous layer to the next layer, we pass identity of previous layer output and input. Due to this, the model can decide for itself how much of the previous layer output is necessary, thus pseudo-adapting their depth.
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Sisodia, S.P.S., Sisodia, R.P.S., Bharath, K.P., Muthu, R.K. (2021). Tagging of Weakly Labeled Acoustic Data Using Skip Layer Connection Detection Classification Model. In: Komanapalli, V.L.N., Sivakumaran, N., Hampannavar, S. (eds) Advances in Automation, Signal Processing, Instrumentation, and Control. i-CASIC 2020. Lecture Notes in Electrical Engineering, vol 700. Springer, Singapore. https://doi.org/10.1007/978-981-15-8221-9_5
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DOI: https://doi.org/10.1007/978-981-15-8221-9_5
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