Search Results for author: Dongsub Shim

Found 7 papers, 3 papers with code

Code Models are Zero-shot Precondition Reasoners

no code implementations16 Nov 2023 Lajanugen Logeswaran, Sungryull Sohn, Yiwei Lyu, Anthony Zhe Liu, Dong-Ki Kim, Dongsub Shim, Moontae Lee, Honglak Lee

One of the fundamental skills required for an agent acting in an environment to complete tasks is the ability to understand what actions are plausible at any given point.

Decision Making

Preserving Linear Separability in Continual Learning by Backward Feature Projection

1 code implementation CVPR 2023 Qiao Gu, Dongsub Shim, Florian Shkurti

To achieve a better stability-plasticity trade-off, we propose Backward Feature Projection (BFP), a method for continual learning that allows the new features to change up to a learnable linear transformation of the old features.

Continual Learning Knowledge Distillation

Towards More Objective Evaluation of Class Incremental Learning: Representation Learning Perspective

no code implementations16 Jun 2022 Sungmin Cha, Jihwan Kwak, Dongsub Shim, Hyunwoo Kim, Moontae Lee, Honglak Lee, Taesup Moon

While the common method for evaluating CIL algorithms is based on average test accuracy for all learned classes, we argue that maximizing accuracy alone does not necessarily lead to effective CIL algorithms.

Class Incremental Learning Incremental Learning +2

ExCon: Explanation-driven Supervised Contrastive Learning for Image Classification

1 code implementation28 Nov 2021 Zhibo Zhang, Jongseong Jang, Chiheb Trabelsi, Ruiwen Li, Scott Sanner, Yeonjeong Jeong, Dongsub Shim

Contrastive learning has led to substantial improvements in the quality of learned embedding representations for tasks such as image classification.

Adversarial Robustness Classification +2

Online Class-Incremental Continual Learning with Adversarial Shapley Value

2 code implementations31 Aug 2020 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.

Continual Learning Open-Ended Question Answering

Cannot find the paper you are looking for? You can Submit a new open access paper.