Title: Controllable Concept Transfer of Intermediate Representations. Abstract: With the proliferation of large pre-trained models in various domains, transfer learning has gained prominence where intermediate representations from these models can be leveraged to train better (target) task-specific models, with possibly limited labeled data. Although transfer learning can be beneficial in many cases, it can also transfer undesirable information to target tasks that may severely curtail its performance in the target domain or raise ethical concerns related to privacy and/or fairness. In this paper, we propose a novel approach for controlling the transfer of user-determined semantic concepts (viz. color, glasses, etc.) in intermediate source representations to target tasks without the need to retrain the source model which can otherwise be expensive or even infeasible. Notably, this is also a bigger challenge than blocking concepts in the input representation as a given intermediate source representation is biased towards the source task it was originally trained to solve, thus possibly further entangling the desired concepts. We qualitatively and quantitatively evaluate our approach in the visual domain showcasing its efficacy for classification and generative source models. 