3 citations · 5 across the 9 of their papers we have counts for
11 papers
MoNe: Modular Neural Memory for Efficient Long Context Inference
Wonguk Cho, Kyubyung Chae, Tribhuvanesh Orekondy +6
We present MoNe, a lightweight modular neural memory that attaches to any frozen pretrained Transformer to enable long-context inference without retraining. MoNe reads context in f…
Memory-Efficient Fine-Tuning Diffusion Transformers via Dynamic Patch Sampling and Block Skipping
Sunghyun Park, Jeongho Kim, Hyoungwoo Park +6
Diffusion Transformers (DiTs) have significantly enhanced text-to-image (T2I) generation quality, enabling high-quality personalized content creation. However, fine-tuning these mo…
Memory-Efficient Personalization of Text-to-Image Diffusion Models via Selective Optimization Strategies
Seokeon Choi, Sunghyun Park, Hyoungwoo Park +2
Memory-efficient personalization is critical for adapting text-to-image diffusion models while preserving user privacy and operating within the limited computational resources of e…
Steering Guidance for Personalized Text-to-Image Diffusion Models
Sunghyun Park, Seokeon Choi, Hyoungwoo Park +1
Personalizing text-to-image diffusion models is crucial for adapting the pre-trained models to specific target concepts, enabling diverse image generation. However, fine-tuning wit…
From Wardrobe to Canvas: Wardrobe Polyptych LoRA for Part-level Controllable Human Image Generation
Jeongho Kim, Sunghyun Park, Hyoungwoo Park +3
Recent diffusion models achieve personalization by learning specific subjects, allowing learned attributes to be integrated into generated images. However, personalized human image…
ConsNoTrainLoRA: Data-driven Weight Initialization of Low-rank Adapters using Constraints
Debasmit Das, Hyoungwoo Park, Munawar Hayat +3
Foundation models are pre-trained on large-scale datasets and subsequently fine-tuned on small-scale datasets using parameter-efficient fine-tuning (PEFT) techniques like low-rank…
Domain Agnostic Few-shot Learning for Speaker Verification
Seunghan Yang, Debasmit Das, Janghoon Cho +2
Deep learning models for verification systems often fail to generalize to new users and new environments, even though they learn highly discriminative features. To address this pro…
SubSpectral Normalization for Neural Audio Data Processing
Simyung Chang, Hyoungwoo Park, Janghoon Cho +3
Convolutional Neural Networks are widely used in various machine learning domains. In image processing, the features can be obtained by applying 2D convolution to all spatial dimen…
End-to-End Lane Marker Detection via Row-wise Classification
Seungwoo Yoo, Heeseok Lee, Heesoo Myeong +4
In autonomous driving, detecting reliable and accurate lane marker positions is a crucial yet challenging task. The conventional approaches for the lane marker detection problem pe…
Weakly Labeled Sound Event Detection Using Tri-training and Adversarial Learning
Hyoungwoo Park, Sungrack Yun, Jungyun Eum +2
This paper considers a semi-supervised learning framework for weakly labeled polyphonic sound event detection problems for the DCASE 2019 challenge's task4 by combining both the tr…
Acoustic Scene Classification Based on a Large-margin Factorized CNN
Janghoon Cho, Sungrack Yun, Hyoungwoo Park +2
In this paper, we present an acoustic scene classification framework based on a large-margin factorized convolutional neural network (CNN). We adopt the factorized CNN to learn the…