Adaptive pooling operators for multiple instance learning
Adaptive pooling operators for Multiple Instance Learning (documentation).
AutoPool is an adaptive (trainable) pooling operator which smoothly interpolates between common pooling operators, such as min-, max-, or average-pooling, automatically adapting to the characteristics of the data.
AutoPool can be readily applied to any differentiable model for time-series label prediction. AutoPool is presented in the following paper, where it is evaluated in conjunction with convolutional neural networks for Sound Event Detection:
Adaptive pooling operators for weakly labeled sound event detection
Brian Mcfee, Justin Salamon, and Juan Pablo Bello
IEEE Transactions on Audio, Speech and Language Processing, In press, 2018.
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