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We propose a new algorithm for training deep neural networks (DNNs) withbinary weights. In particular, we first cast the problem of training binaryneural networks (BiNNs) as a bilevel optimization instance and subsequentlyconstruct flexible relaxations of this bilevel program. The resulting trainingmethod shares its algorithmic simplicity with several existing approaches totrain BiNNs, in particular with the straight-through gradient estimatorsuccessfully employed in BinaryConnect and subsequent methods. In fact, ourproposed method can be interpreted as an adaptive variant of the originalstraight-through estimator that conditionally (but not always) acts like alinear mapping in the backward pass of error propagation. Experimental resultsdemonstrate that our new algorithm offers favorable performance compared toexisting approaches.

AdaSTE: An Adaptive Straight-Through Estimator to Train Binary Neural Networks