Computer implemented augmented machine learning system comprising a parameterized input transformation module and a pretrained machine learning system, wherein the input transformation module comprises a further machine learning system, a linear transformation module and an addition module, wherein an output of the parameterized input transformation module is connected with the input of the pretrained machine learning system, wherein the pretrained machine learning system is configured to classify an image, wherein the input transformation module is configured to at least linearly transform its input that is the image into a transformed input and to input the transformed input to the pretrained machine learning system, wherein the input transformation module is configured to at least partially undo a domain shift of its inputs such that its outputs comes relatively close to the original inputs of the training data distribution on which the pretrained machine learning system has been trained, wherein the further machine learning system ( r ψ) is connected in series with the linear transformation module (24,25), wherein the further machine learning system ( r ψ) is configured to non-linearly transform the input of the input transformation module, wherein the linear transformation module (24,25) is configured to linearly transform its input depending on parameters ( γ, β) characterizing a linear transformation, characterized in that the addition module is interconnected between the further machine learning system ( r ψ) and the linear transformation module (24,25), and that the addition module comprises at least two inputs including a first input that is configured to receive outputs of the further machine learning system ( r ψ) and a second input that is configured to receive the input of the input transformation module, and that the addition module is configured to compute a weighted sum of its inputs and to input the weighted sum to the linear transformation module. wherein the input transformation module is configured to at least partially undo a domain shift of its inputs such that its outputs comes relatively close to the original inputs of the training data distribution on which the pretrained machine learning system has been trained, wherein the further machine learning system ( r ψ) is connected in series with the linear transformation module (24,25), wherein the further machine learning system ( r ψ) is configured to non-linearly transform the input of the input transformation module, wherein the linear transformation module (24,25) is configured to linearly transform its input depending on parameters ( γ, β) characterizing a linear transformation, characterized in that the addition module is interconnected between the further machine learning system ( r ψ) and the linear transformation module (24,25), and that the addition module comprises at least two inputs including a first input that is configured to receive outputs of the further machine learning system ( r ψ) and a second input that is configured to receive the input of the input transformation module, and that the addition module is configured to compute a weighted sum of its inputs and to input the weighted sum to the linear transformation module.