  Adaptive loss function formulation is an active area of research and has
gained a great deal of popularity in recent years, following the success of
deep learning. However, existing frameworks of adaptive loss functions often
suffer from slow convergence and poor choice of weights for the loss
components. Traditionally, the elements of a multi-part loss function are
weighted equally or their weights are determined through heuristic approaches
that yield near-optimal (or sub-optimal) results. To address this problem, we
propose a family of methods, called SoftAdapt, that dynamically change function
weights for multi-part loss functions based on live performance statistics of
the component losses. SoftAdapt is mathematically intuitive, computationally
efficient and straightforward to implement. In this paper, we present the
mathematical formulation and pseudocode for SoftAdapt, along with results from
applying our methods to image reconstruction (Sparse Autoencoders) and
synthetic data generation (Introspective Variational Autoencoders).
