Coarse-grained pooled features obtained from convolutional autoencoder (CAE) achieve scale and shift invariances and have been widely used recently.However

in most previous works coarse-grained pooled features are obtained by empirically modulating parameters in CAE.In this paper

we see the CAE as a whole

find the probabilistic factors affecting the performance of it

and formulate a general framework to regulate parameters in it to obtain better coarse-grained representation.Firstly

the discrimination-invariance tradeoff of coarse-grained features is probabilistically evaluated in the pooled feature maps.Furthermore

the proper convolved filter scales and appropriate whitening parameters are suggested in a CAE.Secondly

pooling approaches are combined with the sparsity degree in pooling regions

and we propose the preferable pooling approach in different cases.Experimental results on two independent benchmark datasets (STL-10 and CIFAR-10) demonstrate that our framework can guide CAEs to extract better coarse-grained pooled features and performs better in multi-class classification task.