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#ICLR2021 cam-ready II: "LiftPool: Bidirectional ConvNet Pooling" w/ Jiaojiao Zhao is now available: isis-data.science.uva.nl/cgmsnoek/pub/z… No more lossy down- and upsampling when pooling! 1/n 
LiftPool adopts the philosophy of the classical #Lifting #Scheme from #signal #processing. LiftDownPool decomposes a feature map into various downsized sub-bands, each of which contains information with different frequencies. Because of its invertible properties, ... 2/n
by performing LiftDownPool backwards, a corresponding up-pooling layer #LiftUpPool is able to generate a refined upsampled feature map using the detail sub-bands, which is useful for #image-#to-#image #translation challenges. 3/n
