17 citations · 47 across the 13 of their papers we have counts for
14 papers
PUBG Ally: A Conversational Embodied Agent as an AI Teammate
Beomsoo Kim, PUBG Ally Team, Byeongju Kim +22
We introduce PUBG Ally, an embodied agent for PUBG: BATTLEGROUNDS that can reason, act autonomously, and play alongside players as a voice-enabled teammate. Building such a teammat…
Nearly Zero-Cost Protection Against Mimicry by Personalized Diffusion Models
Namhyuk Ahn, KiYoon Yoo, Wonhyuk Ahn +2
Recent advancements in diffusion models revolutionize image generation but pose risks of misuse, such as replicating artworks or generating deepfakes. Existing image protection met…
Imperceptible Protection against Style Imitation from Diffusion Models
Namhyuk Ahn, Wonhyuk Ahn, KiYoon Yoo +2
Recent progress in diffusion models has profoundly enhanced the fidelity of image generation, but it has raised concerns about copyright infringements. While prior methods have int…
Open Domain Generalization with a Single Network by Regularization Exploiting Pre-trained Features
Inseop Chung, KiYoon Yoo, Nojun Kwak
Open Domain Generalization (ODG) is a challenging task as it not only deals with distribution shifts but also category shifts between the source and target datasets. To handle this…
Advancing Beyond Identification: Multi-bit Watermark for Large Language Models
KiYoon Yoo, Wonhyuk Ahn, Nojun Kwak
We show the viability of tackling misuses of large language models beyond the identification of machine-generated text. While existing zero-bit watermark methods focus on detection…
Robust Multi-bit Natural Language Watermarking through Invariant Features
KiYoon Yoo, Wonhyuk Ahn, Jiho Jang +1
Recent years have witnessed a proliferation of valuable original natural language contents found in subscription-based media outlets, web novel platforms, and outputs of large lang…
Backdoor Attacks in Federated Learning by Rare Embeddings and Gradient Ensembling
KiYoon Yoo, Nojun Kwak
Recent advances in federated learning have demonstrated its promising capability to learn on decentralized datasets. However, a considerable amount of work has raised concerns due…
Detection of Word Adversarial Examples in Text Classification: Benchmark and Baseline via Robust Density Estimation
KiYoon Yoo, Jangho Kim, Jiho Jang +1
Word-level adversarial attacks have shown success in NLP models, drastically decreasing the performance of transformer-based models in recent years. As a countermeasure, adversaria…
Self-Distilled Self-Supervised Representation Learning
Jiho Jang, Seonhoon Kim, Kiyoon Yoo +3
State-of-the-art frameworks in self-supervised learning have recently shown that fully utilizing transformer-based models can lead to performance boost compared to conventional CNN…
Self-Evolutionary Optimization for Pareto Front Learning
Simyung Chang, KiYoon Yoo, Jiho Jang +1
Multi-task learning (MTL), which aims to improve performance by learning multiple tasks simultaneously, inherently presents an optimization challenge due to multiple objectives. He…
Dynamic Collective Intelligence Learning: Finding Efficient Sparse Model via Refined Gradients for Pruned Weights
Jangho Kim, Jayeon Yoo, Yeji Song +2
With the growth of deep neural networks (DNN), the number of DNN parameters has drastically increased. This makes DNN models hard to be deployed on resource-limited embedded system…
Edge Bias in Federated Learning and its Solution by Buffered Knowledge Distillation
Sangho Lee, Kiyoon Yoo, Nojun Kwak
Federated learning (FL), which utilizes communication between the server (core) and local devices (edges) to indirectly learn from more data, is an emerging field in deep learning…
On the Orthogonality of Knowledge Distillation with Other Techniques: From an Ensemble Perspective
SeongUk Park, KiYoon Yoo, Nojun Kwak
To put a state-of-the-art neural network to practical use, it is necessary to design a model that has a good trade-off between the resource consumption and performance on the test…
Position-based Scaled Gradient for Model Quantization and Pruning
Jangho Kim, KiYoon Yoo, Nojun Kwak
We propose the position-based scaled gradient (PSG) that scales the gradient depending on the position of a weight vector to make it more compression-friendly. First, we theoretica…