61 citations · 193 across the 17 of their papers we have counts for
17 papers
BAPO: Base-Anchored Preference Optimization for Overcoming Forgetting in Large Language Models Personalization
Gihun Lee, Minchan Jeong, Yujin Kim +4
While learning to align Large Language Models (LLMs) with human preferences has shown remarkable success, aligning these models to meet the diverse user preferences presents furthe…
FedFN: Feature Normalization for Alleviating Data Heterogeneity Problem in Federated Learning
Seongyoon Kim, Gihun Lee, Jaehoon Oh +1
Federated Learning (FL) is a collaborative method for training models while preserving data privacy in decentralized settings. However, FL encounters challenges related to data het…
Cross-Modal Retrieval Meets Inference:Improving Zero-Shot Classification with Cross-Modal Retrieval
Seongha Eom, Namgyu Ho, Jaehoon Oh +1
Contrastive language-image pre-training (CLIP) has demonstrated remarkable zero-shot classification ability, namely image classification using novel text labels. Existing works hav…
FedSOL: Stabilized Orthogonal Learning with Proximal Restrictions in Federated Learning
Gihun Lee, Minchan Jeong, Sangmook Kim +2
Federated Learning (FL) aggregates locally trained models from individual clients to construct a global model. While FL enables learning a model with data privacy, it often suffers…
Synergy with Translation Artifacts for Training and Inference in Multilingual Tasks
Jaehoon Oh, Jongwoo Ko, Se-Young Yun
Translation has played a crucial role in improving the performance on multilingual tasks: (1) to generate the target language data from the source language data for training and (2…
Demystifying the Base and Novel Performances for Few-shot Class-incremental Learning
Jaehoon Oh, Se-Young Yun
Few-shot class-incremental learning (FSCIL) has addressed challenging real-world scenarios where unseen novel classes continually arrive with few samples. In these scenarios, it is…
How to Fine-tune Models with Few Samples: Update, Data Augmentation, and Test-time Augmentation
Yujin Kim, Jaehoon Oh, Sungnyun Kim +1
Most of the recent few-shot learning (FSL) algorithms are based on transfer learning, where a model is pre-trained using a large amount of source data, and the pre-trained model is…
ReFine: Re-randomization before Fine-tuning for Cross-domain Few-shot Learning
Jaehoon Oh, Sungnyun Kim, Namgyu Ho +3
Cross-domain few-shot learning (CD-FSL), where there are few target samples under extreme differences between source and target domains, has recently attracted huge attention. Rece…
Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty
Jaehoon Oh, Sungnyun Kim, Namgyu Ho +3
Cross-domain few-shot learning (CD-FSL) has drawn increasing attention for handling large differences between the source and target domains--an important concern in real-world scen…
FedBABU: Towards Enhanced Representation for Federated Image Classification
Jaehoon Oh, Sangmook Kim, Se-Young Yun
Federated learning has evolved to improve a single global model under data heterogeneity (as a curse) or to develop multiple personalized models using data heterogeneity (as a bles…
Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation
Taehyeon Kim, Jaehoon Oh, NakYil Kim +2
Knowledge distillation (KD), transferring knowledge from a cumbersome teacher model to a lightweight student model, has been investigated to design efficient neural architectures.…
Accurate and Fast Federated Learning via IID and Communication-Aware Grouping
Jin-woo Lee, Jaehoon Oh, Yooju Shin +2
Federated learning has emerged as a new paradigm of collaborative machine learning; however, it has also faced several challenges such as non-independent and identically distribute…
TornadoAggregate: Accurate and Scalable Federated Learning via the Ring-Based Architecture
Jin-woo Lee, Jaehoon Oh, Sungsu Lim +2
Federated learning has emerged as a new paradigm of collaborative machine learning; however, many prior studies have used global aggregation along a star topology without much cons…
BOIL: Towards Representation Change for Few-shot Learning
Jaehoon Oh, Hyungjun Yoo, ChangHwan Kim +1
Model Agnostic Meta-Learning (MAML) is one of the most representative of gradient-based meta-learning algorithms. MAML learns new tasks with a few data samples using inner updates…
SIPA: A Simple Framework for Efficient Networks
Gihun Lee, Sangmin Bae, Jaehoon Oh +1
With the success of deep learning in various fields and the advent of numerous Internet of Things (IoT) devices, it is essential to lighten models suitable for low-power devices. I…
Spectrogram-channels u-net: a source separation model viewing each channel as the spectrogram of each source
Jaehoon Oh, Duyeon Kim, Se-Young Yun
Sound source separation has attracted attention from Music Information Retrieval(MIR) researchers, since it is related to many MIR tasks such as automatic lyric transcription, sing…
TED Talk Recommender Using Speech Transcripts
Jaehoon Oh, Injung Lee, Yeon Seonwoo +3
Nowadays, online video platforms mostly recommend related videos by analyzing user-driven data such as viewing patterns, rather than the content of the videos. However, content is…