72 citations · 330 across the 48 of their papers we have counts for
7 papers · 1 filter
In Search of a Data Transformation That Accelerates Neural Field Training
Junwon Seo, Sangyoon Lee, Kwang In Kim +1
Neural field is an emerging paradigm in data representation that trains a neural network to approximate the given signal. A key obstacle that prevents its widespread adoption is th…
Communication-Efficient Split Learning via Adaptive Feature-Wise Compression
Yongjeong Oh, Jaeho Lee, Christopher G. Brinton +1
This paper proposes a novel communication-efficient split learning (SL) framework, named SplitFC, which reduces the communication overhead required for transmitting intermediate fe…
Debiased Distillation by Transplanting the Last Layer
Jiwoon Lee, Jaeho Lee
Deep models are susceptible to learning spurious correlations, even during the post-processing. We take a closer look at the knowledge distillation -- a popular post-processing tec…
The Role of Masking for Efficient Supervised Knowledge Distillation of Vision Transformers
Seungwoo Son, Jegwang Ryu, Namhoon Lee +1
Knowledge distillation is an effective method for training lightweight vision models. However, acquiring teacher supervision for training samples is often costly, especially from l…
Learning Large-scale Neural Fields via Context Pruned Meta-Learning
Jihoon Tack, Subin Kim, Sihyun Yu +3
We introduce an efficient optimization-based meta-learning technique for large-scale neural field training by realizing significant memory savings through automated online context…
Discovering and Mitigating Visual Biases through Keyword Explanation
Younghyun Kim, Sangwoo Mo, Minkyu Kim +3
Addressing biases in computer vision models is crucial for real-world AI deployments. However, mitigating visual biases is challenging due to their unexplainable nature, often iden…
Modality-Agnostic Variational Compression of Implicit Neural Representations
Jonathan Richard Schwarz, Jihoon Tack, Yee Whye Teh +2
We introduce a modality-agnostic neural compression algorithm based on a functional view of data and parameterised as an Implicit Neural Representation (INR). Bridging the gap betw…