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Jongseong Jang

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4 papers

1 author row

PRL Workshop 2023 Workshop Paper

A Learnable Similarity Metric for Transfer Learning with Dynamics Mismatch

Ram Ananth Sreenivasan
Hyun-Rok Lee
Yeonjeong Jeong
Jongseong Jang
Dongsub Shim
Chi-Guhn Lee

When transferring knowledge from previously mastered source tasks to a new target task, the similarity between the source and target tasks can play a key role in whether such transfer is beneficial or harmful. In this paper, we develop an upper-bound of difference in action value function of source and target tasks with dynamics mismatch, and use the bound as a metric for dissimilarity between two tasks. The proposed metric does not require additional samples and adds little extra computation to the reinforcement learning algorithm for the target task. Also, the metric is highly portable so that it can be integrated into a wide range of algorithms. We showcase the effectiveness of the metric by incorporating it as a gatekeeper in the knowledge transfer step of transfer reinforcement learning algorithms. Numerical results on a suite of transfer learning scenarios demonstrate the benefits of preventing negative transfer in case of severe mismatch while accelerating learning otherwise

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IJCAI Conference 2022 Conference Paper

Multi-policy Grounding and Ensemble Policy Learning for Transfer Learning with Dynamics Mismatch

Hyun-Rok Lee
Ram Ananth Sreenivasan
Yeonjeong Jeong
Jongseong Jang
Dongsub Shim
Chi-Guhn Lee

We propose a new transfer learning algorithm between tasks with different dynamics. The proposed algorithm solves an Imitation from Observation problem (IfO) to ground the source environment to the target task before learning an optimal policy in the grounded environment. The learned policy is deployed in the target task without additional training. A particular feature of our algorithm is the employment of multiple rollout policies during training with a goal to ground the environment more globally; hence, it is named as Multi-Policy Grounding (MPG). The quality of final policy is further enhanced via ensemble policy learning. We demonstrate the superiority of the proposed algorithm analytically and numerically. Numerical studies show that the proposed multi-policy approach allows comparable grounding with single policy approach with a fraction of target samples, hence the algorithm is able to maintain the quality of obtained policy even as the number of interactions with the target environment becomes extremely small.

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AAAI Conference 2021 Conference Paper

Explaining Convolutional Neural Networks through Attribution-Based Input Sampling and Block-Wise Feature Aggregation

Sam Sattarzadeh
Mahesh Sudhakar
Anthony Lem
Shervin Mehryar
Konstantinos N Plataniotis
Jongseong Jang
Hyunwoo Kim
Yeonjeong Jeong

As an emerging field in Machine Learning, Explainable AI (XAI) has been offering remarkable performance in interpreting the decisions made by Convolutional Neural Networks (CNNs). To achieve visual explanations for CNNs, methods based on class activation mapping and randomized input sampling have gained great popularity. However, the attribution methods based on these techniques provide low-resolution and blurry explanation maps that limit their explanation ability. To circumvent this issue, visualization based on various layers is sought. In this work, we collect visualization maps from multiple layers of the model based on an attributionbased input sampling technique and aggregate them to reach a fine-grained and complete explanation. We also propose a layer selection strategy that applies to the whole family of CNN-based models, based on which our extraction framework is applied to visualize the last layers of each convolutional block of the model. Moreover, we perform an empirical analysis of the efficacy of derived lower-level information to enhance the represented attributions. Comprehensive experiments conducted on shallow and deep models trained on natural and industrial datasets, using both ground-truth and model-truth based evaluation metrics validate our proposed algorithm by meeting or outperforming the state-of-the-art methods in terms of explanation ability and visual quality, demonstrating that our method shows stability regardless of the size of objects or instances to be explained.

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AAAI Conference 2021 Conference Paper

Online Class-Incremental Continual Learning with Adversarial Shapley Value

Dongsub Shim
Zheda Mai
Jihwan Jeong
Scott Sanner
Hyunwoo Kim
Jongseong Jang

As image-based deep learning becomes pervasive on every device from cell phones to smart watches, there is a growing need to develop methods that continually learn from data while minimizing memory footprint and power consumption. While memory replay techniques have shown exceptional promise for this task of continual learning, the best method for selecting which buffered images to replay is still an open question. In this paper, we specifically focus on the online class-incremental setting where a model needs to learn new classes continually from an online data stream. To this end, we contribute a novel Adversarial Shapley value scoring method that scores memory data samples according to their ability to preserve latent decision boundaries for previously observed classes (to maintain learning stability and avoid forgetting) while interfering with latent decision boundaries of current classes being learned (to encourage plasticity and optimal learning of new class boundaries). Overall, we observe that our proposed ASER method provides competitive or improved performance compared to state-of-the-art replaybased continual learning methods on a variety of datasets.

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