Search Results for author: Kyunghoon Bae

Found 8 papers, 2 papers with code

Instruction Matters, a Simple yet Effective Task Selection Approach in Instruction Tuning for Specific Tasks

no code implementations25 Apr 2024 Changho Lee, Janghoon Han, Seonghyeon Ye, Stanley Jungkyu Choi, Honglak Lee, Kyunghoon Bae

Instruction tuning has shown its ability to not only enhance zero-shot generalization across various tasks but also its effectiveness in improving the performance of specific tasks.

Zero-shot Generalization

ReConPatch : Contrastive Patch Representation Learning for Industrial Anomaly Detection

1 code implementation26 May 2023 Jeeho Hyun, Sangyun Kim, Giyoung Jeon, Seung Hwan Kim, Kyunghoon Bae, Byung Jun Kang

In this paper, we introduce ReConPatch, which constructs discriminative features for anomaly detection by training a linear modulation of patch features extracted from the pre-trained model.

Anomaly Detection Contrastive Learning +2

Significantly Improving Zero-Shot X-ray Pathology Classification via Fine-tuning Pre-trained Image-Text Encoders

no code implementations14 Dec 2022 Jongseong Jang, Daeun Kyung, Seung Hwan Kim, Honglak Lee, Kyunghoon Bae, Edward Choi

However, large-scale and high-quality data to train powerful neural networks are rare in the medical domain as the labeling must be done by qualified experts.

Classification Contrastive Learning +2

ANNA: Enhanced Language Representation for Question Answering

no code implementations28 Mar 2022 Changwook Jun, Hansol Jang, Myoseop Sim, Hyun Kim, Jooyoung Choi, Kyungkoo Min, Kyunghoon Bae

Pre-trained language models have brought significant improvements in performance in a variety of natural language processing tasks.

Language Modelling Question Answering

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

no code implementations1 Oct 2020 Sam Sattarzadeh, Mahesh Sudhakar, Anthony Lem, Shervin Mehryar, K. N. Plataniotis, Jongseong Jang, Hyunwoo Kim, Yeonjeong Jeong, Sangmin Lee, Kyunghoon Bae

In this work, we collect visualization maps from multiple layers of the model based on an attribution-based input sampling technique and aggregate them to reach a fine-grained and complete explanation.

Explainable Artificial Intelligence (XAI)

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