33 citations · 48 across the 7 of their papers we have counts for
9 papers
SelecMix: Debiased Learning by Contradicting-pair Sampling
Inwoo Hwang, Sangjun Lee, Yunhyeok Kwak +4
Neural networks trained with ERM (empirical risk minimization) sometimes learn unintended decision rules, in particular when their training data is biased, i.e., when training labe…
Modal-specific Pseudo Query Generation for Video Corpus Moment Retrieval
Minjoon Jung, Seongho Choi, Joochan Kim +2
Video corpus moment retrieval (VCMR) is the task to retrieve the most relevant video moment from a large video corpus using a natural language query. For narrative videos, e.g., dr…
AlphaTuning: Quantization-Aware Parameter-Efficient Adaptation of Large-Scale Pre-Trained Language Models
Se Jung Kwon, Jeonghoon Kim, Jeongin Bae +7
There are growing interests in adapting large-scale language models using parameter-efficient fine-tuning methods. However, accelerating the model itself and achieving better infer…
Mutual Information Divergence: A Unified Metric for Multimodal Generative Models
Jin-Hwa Kim, Yunji Kim, Jiyoung Lee +2
Text-to-image generation and image captioning are recently emerged as a new experimental paradigm to assess machine intelligence. They predict continuous quantity accompanied by th…
Semi-orthogonal Embedding for Efficient Unsupervised Anomaly Segmentation
Jin-Hwa Kim, Do-Hyeong Kim, Saehoon Yi +1
We present the efficiency of semi-orthogonal embedding for unsupervised anomaly segmentation. The multi-scale features from pre-trained CNNs are recently used for the localized Mah…
Multi-step Estimation for Gradient-based Meta-learning
Jin-Hwa Kim, Junyoung Park, Yongseok Choi
Gradient-based meta-learning approaches have been successful in few-shot learning, transfer learning, and a wide range of other domains. Despite its efficacy and simplicity, the bu…