Transferring Knowledge across Learning Processes
In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.
 In this paper, the authors study an important transfer learning problem, i.e., knowledge transfer between distinct tasks, which is usually called *far transfer* (instead of *near transfer*). Specifically, the authors propose a lightweight framework called Leap, which aims to achieve knowledge transfer *across learning processes*. In particular, a method for meta-learning (see Algorithm 1) is developed, which focuses on minimizing *the expected length of the path* (see the corresponding term in Eqs.(4-6)). Empirical studies on three public datasets show the effectiveness of the proposed method. Overall, the paper is well presented. Some comments/suggestions: (i) The details of the experiments such as parameter configurations are missing, which makes the results not easy to be reproduced. (ii) For the baseline methods used in the experiments, the authors are suggested to include more state-of-the-art transfer learning methods in order to make the results more convincing. (iii) Finally, if the authors can use some commonly used datasets in existing transfer learning works, the comparative results will be more interesting.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Transferring Knowledge across Learning Processes
In complex transfer learning scenarios new tasks might not be tightly linked to previous tasks. Approaches that transfer information contained only in the final parameters of a source model will therefore struggle. Instead, transfer learning at at higher level of abstraction is needed. We propose Leap, a framework that achieves this by transferring knowledge across learning processes. We associate each task with a manifold on which the training process travels from initialization to final parameters and construct a meta-learning objective that minimizes the expected length of this path. Our framework leverages only information obtained during training and can be computed on the fly at negligible cost. We demonstrate that our framework outperforms competing methods, both in meta-learning and transfer learning, on a set of computer vision tasks. Finally, we demonstrate that Leap can transfer knowledge across learning processes in demanding reinforcement learning environments (Atari) that involve millions of gradient steps.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



 In this paper, the authors study an important transfer learning problem, i.e., knowledge transfer between distinct tasks, which is usually called *far transfer* (instead of *near transfer*). Specifically, the authors propose a lightweight framework called Leap, which aims to achieve knowledge transfer *across learning processes*. In particular, a method for meta-learning (see Algorithm 1) is developed, which focuses on minimizing *the expected length of the path* (see the corresponding term in Eqs.(4-6)). Empirical studies on three public datasets show the effectiveness of the proposed method. Overall, the paper is well presented. Some comments/suggestions: (i) The details of the experiments such as parameter configurations are missing, which makes the results not easy to be reproduced. (ii) For the baseline methods used in the experiments, the authors are suggested to include more state-of-the-art transfer learning methods in order to make the results more convincing. (iii) Finally, if the authors can use some commonly used datasets in existing transfer learning works, the comparative results will be more interesting.
assistant
 In this paper, the authors study an important transfer learning problem, i.e., knowledge transfer between distinct tasks, which is usually called *far transfer* (instead of *near transfer*). Specifically, the authors propose a lightweight framework called Leap, which aims to achieve knowledge transfer *across learning processes*. In particular, a method for meta-learning (see Algorithm 1) is developed, which focuses on minimizing *the expected length of the path* (see the corresponding term in Eqs.(4-6)). Empirical studies on three public datasets show the effectiveness of the proposed method. Overall, the paper is well presented. Some comments/suggestions: (i) The details of the experiments such as parameter configurations are missing, which makes the results not easy to be reproduced. (ii) For the baseline methods used in the experiments, the authors are suggested to include more state-of-the-art transfer learning methods in order to make the results more convincing. (iii) Finally, if the authors can use some commonly used datasets in existing transfer learning works, the comparative results will be more interesting.