7 citations · 18 across the 10 of their papers we have counts for
11 papers
Temperature-Free Loss Function for Contrastive Learning
Bum Jun Kim, Sang Woo Kim
As one of the most promising methods in self-supervised learning, contrastive learning has achieved a series of breakthroughs across numerous fields. A predominant approach to impl…
Stochastic Subsampling With Average Pooling
Bum Jun Kim, Sang Woo Kim
Regularization of deep neural networks has been an important issue to achieve higher generalization performance without overfitting problems. Although the popular method of Dropout…
The Disappearance of Timestep Embedding in Modern Time-Dependent Neural Networks
Bum Jun Kim, Yoshinobu Kawahara, Sang Woo Kim
Dynamical systems are often time-varying, whose modeling requires a function that evolves with respect to time. Recent studies such as the neural ordinary differential equation pro…
Configuring Data Augmentations to Reduce Variance Shift in Positional Embedding of Vision Transformers
Bum Jun Kim, Sang Woo Kim
Vision transformers (ViTs) have demonstrated remarkable performance in a variety of vision tasks. Despite their promising capabilities, training a ViT requires a large amount of di…
Scale Equalization for Multi-Level Feature Fusion
Bum Jun Kim, Sang Woo Kim
Deep neural networks have exhibited remarkable performance in a variety of computer vision fields, especially in semantic segmentation tasks. Their success is often attributed to m…
Analysis of NaN Divergence in Training Monocular Depth Estimation Model
Bum Jun Kim, Hyeonah Jang, Sang Woo Kim
The latest advances in deep learning have facilitated the development of highly accurate monocular depth estimation models. However, when training a monocular depth estimation netw…
Resolution-Aware Design of Atrous Rates for Semantic Segmentation Networks
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +1
DeepLab is a widely used deep neural network for semantic segmentation, whose success is attributed to its parallel architecture called atrous spatial pyramid pooling (ASPP). ASPP…
Understanding Gaussian Attention Bias of Vision Transformers Using Effective Receptive Fields
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +1
Vision transformers (ViTs) that model an image as a sequence of partitioned patches have shown notable performance in diverse vision tasks. Because partitioning patches eliminates…
How to Use Dropout Correctly on Residual Networks with Batch Normalization
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +2
For the stable optimization of deep neural networks, regularization methods such as dropout and batch normalization have been used in various tasks. Nevertheless, the correct posit…
On the Ideal Number of Groups for Isometric Gradient Propagation
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +1
Recently, various normalization layers have been proposed to stabilize the training of deep neural networks. Among them, group normalization is a generalization of layer normalizat…
Guidelines for the Regularization of Gammas in Batch Normalization for Deep Residual Networks
Bum Jun Kim, Hyeyeon Choi, Hyeonah Jang +3
L2 regularization for weights in neural networks is widely used as a standard training trick. However, L2 regularization for gamma, a trainable parameter of batch normalization, re…