This challenge intends to explore how to leverage the knowledge learned from previous tasks that could generalize to new task effectively, and also how to efficiently memorize of previously learned tasks. Making the robot behaves like the human with knowledge transfer, association, and combination capabilities.
To our best knowledge, the provided lifelong object recognition dataset is the 1st one that explicitly indicates the task difficulty under the incremental setting, which is able to foster the lifelong/continual/incremental learning in a supervised/semi-supervised manner. Different from previous instance/class-incremental task, the difficulty-incremental learning is to test the model’s capability over continuous learning when faced with multiple environmental factors, such as illumination, occlusion, camera-object distances/angles, clutter, and context information in both low and high dynamic scenes.
Yes. To obtain the consistent and comparable results, you need to train your model and design your own learning algorithm that can train sequentially over provided 9 batches.
For example, when you train the 1st batch of datasets, you can only have access to train/validation data of 1st batch, but need to test over 1-9 batch test datasets; next when you train the model over the 2nd batch of dataset, you can only have access to train/validation data of 2nd batch, but you can also keep some of validation data from 1st batch for learning current model. That means the validation dataset can be kept for the sequential learning task.
This scenario is quite common when the system is deployed as the real-world application, which should be able to update the model day after day, also the system memory is also valuable that we can not keep old dataset on the current systems, sometimes, we only select some coresets of the dataset that we have used. It is non-trivial to pick out the data that can be the summarizations of the encountered data. We constrain our learning/training procedure that approach this kind of real-world scenario.
Yes, you can submit the provided baseline models, but as we have tested, the results are not state-of-the-art methods. The models provided can be used for your quicker familiar with the procedure.
Is there any state-of-the-art methods that can learn continuous learning strategy ?
Yes, we recommend you go through the recent review paper over this research topic: Continual Lifelong Learning with Neural Networks: A Review.
Start: July 17, 2019, midnight
Start: Oct. 8, 2019, 11:59 a.m.
Description: Final Phase
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