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- .. _learnware:
- ==========================================
- Learnware & Reuser
- ==========================================
-
- Learnware and Reuser are related...
-
- Concepts
- ===================
- The learnware paradiam, first introduced by Zhi-Hua Zhou, is defined as a proficiently trained machine learning model accompanied by a specification that allows future users with no prior knowledge of the learnware to identify and reuse it according to their needs.
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- Developers or owners of trained machine learning models can voluntarily submit their models to a learnware marketplace. If the marketplace accepts the model, it assigns a specification to the model and makes it available in the marketplace.
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- Utilizing Learnware in Practice
- -------------------------------
-
- With a learnware marketplace in place, users can tackle machine learning tasks without having to create models from scratch.
-
- Addressing Concerns with Learnware
- ----------------------------------
-
- The learnware approach aims to address several challenges:
-
-
- +------------------------+----------------------------------------------------------------------------------------+
- | Concern | Solution |
- +========================+========================================================================================+
- | Limited training data | Use existing high-quality learnware and require only a small amount of data for |
- | | adaptation or refinement. |
- +------------------------+----------------------------------------------------------------------------------------+
- | Lack of training skills| Leverage existing learnware instead of building a model from scratch. |
- +------------------------+----------------------------------------------------------------------------------------+
- | Catastrophic forgetting| Retain old knowledge in the marketplace as accepted learnware remain available. |
- +------------------------+----------------------------------------------------------------------------------------+
- | Continual learning | Facilitate continuous and lifelong learning with the constant influx of high-quality |
- | | learnware, enriching the knowledge base. |
- +------------------------+----------------------------------------------------------------------------------------+
- | Data privacy and | Ensure data privacy and proprietary protection by having developers only submit |
- | proprietary concerns | models, not their data. |
- +------------------------+----------------------------------------------------------------------------------------+
- | Unplanned tasks | Ensure the availability of helpful learnware for various tasks, unless entirely new |
- | | to all legal developers. |
- +------------------------+----------------------------------------------------------------------------------------+
- | Carbon emissions | Reduce the need to train numerous large models by assembling smaller models that |
- | | provide satisfactory performance. |
- +------------------------+----------------------------------------------------------------------------------------+
-
- Future Work and Progress
- ------------------------
-
- Despite the promising potential of the learnware proposal, much work remains to bring it to fruition. The following sections will discuss some of the progress made thus far.
-
-
- Learnware for Hetero Reuse (Feature Aligh + Hetero Map Learnware)
- =======================================================================
-
- All Reuse Methods
- ===========================
-
- JobSelectorReuser
- --------------------
-
- The ``JobSelectorReuser`` is a class that inherits from the base reuse class ``BaseReuser``.
- Its purpose is to create a job selector that identifies the optimal learnware for each data point in user data.
- There are three parameters required to initialize the class:
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- - ``learnware_list``: A list of objects of type ``Learnware``. Each ``Learnware`` object should have an RKME specification.
- - ``herding_num``: An optional integer that specifies the number of items to herd, which defaults to 1000 if not provided.
- - ``use_herding``: A boolean flag indicating whether to use kernel herding.
-
- The job selector is essentially a multi-class classifier :math:`g(\boldsymbol{x}):\mathcal{X}\rightarrow \mathcal{I}` with :math:`\mathcal{I}=\{1,\ldots, C\}`, where :math:`C` is the size of ``learnware_list``.
- Given a testing sample :math:`\boldsymbol{x}`, the ``JobSelectorReuser`` predicts it by using the :math:`g(\boldsymbol{x})`-th learnware in ``learnware_list``.
- If ``use_herding`` is set to false, the ``JobSelectorReuser`` uses data points in each learware's RKME spefication with the corresponding learnware index to train a job selector.
- If ``use_herding`` is true, the algorithm estimates the mixture weight based on RKME specifications and raw user data, uses the weight to generate ``herding_num`` auxiliary data points mimicking the user distribution through the kernel herding method, and learns a job selector on these data.
-
-
- AveragingReuser
- ------------------
-
- The ``AveragingReuser`` is a class that inherits from the base reuse class ``BaseReuser``, that implements the average ensemble method by averaging each learnware's output to predict user data.
- There are two parameters required to initialize the class:
-
- - ``learnware_list``: A list of objects of type ``Learnware``.
- - ``mode``: The mode of averaging leanrware outputs, which can be set to "mean" or "vote" and defaults to "mean".
-
- If ``mode`` is set to "mean", the ``AveragingReuser`` computes the mean of the learnware's output to predict user data, which is commonly used in regression tasks.
- If ``mode`` is set to "vote", the ``AveragingReuser`` computes the mean of the softmax of the learnware's output to predict each label probability of user data, which is commonly used in classification tasks.
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