2 citations · 4 across the 10 of their papers we have counts for
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Patch Rebirth: Fast and Transferable Model Inversion of Vision Transformers
Seongsoo Heo, Dong-Wan Choi
Model inversion is a widely adopted technique in data-free learning that reconstructs synthetic inputs from a pretrained model through iterative optimization, without access to ori…
FaceGCD: Generalized Face Discovery via Dynamic Prefix Generation
Yunseok Oh, Dong-Wan Choi
Recognizing and differentiating among both familiar and unfamiliar faces is a critical capability for face recognition systems and a key step toward artificial general intelligence…
Lossless Token Merging Even Without Fine-Tuning in Vision Transformers
Jaeyeon Lee, Dong-Wan Choi
Although Vision Transformers (ViTs) have become the standard architecture in computer vision, their massive sizes lead to significant computational overhead. Token compression tech…
Balanced Online Class-Incremental Learning via Dual Classifiers
Shunjie Wen, Thomas Heinis, Dong-Wan Choi
Online class-incremental learning (OCIL) focuses on gradually learning new classes (called plasticity) from a stream of data in a single-pass, while concurrently preserving knowled…
Training-Free Restoration of Pruned Neural Networks
Keonho Lee, Minsoo Kim, Dong-Wan Choi
Although network pruning has been highly popularized to compress deep neural networks, its resulting accuracy heavily depends on a fine-tuning process that is often computationally…
PGB: One-Shot Pruning for BERT via Weight Grouping and Permutation
Hyemin Lim, Jaeyeon Lee, Dong-Wan Choi
Large pretrained language models such as BERT suffer from slow inference and high memory usage, due to their huge size. Recent approaches to compressing BERT rely on iterative prun…