Open-set Recognition via Augmentation-based Similarity Learning

no code implementations • 24 Mar 2022 • Sepideh Esmaeilpour, Lei Shu, Bing Liu

In many practical scenarios, this is not the case because there are unknowns or unseen class samples in the test data, which is called the open set scenario, and the unknowns need to be detected.

Open Set Learning

Continual Learning Based on OOD Detection and Task Masking

1 code implementation • 17 Mar 2022 • Gyuhak Kim, Sepideh Esmaeilpour, Changnan Xiao, Bing Liu

Existing continual learning techniques focus on either task incremental learning (TIL) or class incremental learning (CIL) problem, but not both.

Class Incremental Learning Incremental Learning +1

Continual Learning Using Pseudo-Replay via Latent Space Sampling

no code implementations • 29 Sep 2021 • Gyuhak Kim, Sepideh Esmaeilpour, Zixuan Ke, Tatsuya Konishi, Bing Liu

PLS is not only simple and efficient but also does not invade data privacy due to the fact that it works in the latent feature space.

Class Incremental Learning Incremental Learning

Zero-Shot Out-of-Distribution Detection Based on the Pre-trained Model CLIP

1 code implementation • 6 Sep 2021 • Sepideh Esmaeilpour, Bing Liu, Eric Robertson, Lei Shu

In an out-of-distribution (OOD) detection problem, samples of known classes(also called in-distribution classes) are used to train a special classifier.

Out-of-Distribution Detection Out of Distribution (OOD) Detection +2

Building an Application Independent Natural Language Interface

no code implementations • 30 Oct 2019 • Sahisnu Mazumder, Bing Liu, Shuai Wang, Sepideh Esmaeilpour

Traditional approaches to building natural language (NL) interfaces typically use a semantic parser to parse the user command and convert it to a logical form, which is then translated to an executable action in an application.