613 citations · 1.2k across the 67 of their papers we have counts for
9 papers · 1 filter
Can We Find Strong Lottery Tickets in Generative Models?
Sangyeop Yeo, Yoojin Jang, Jy-yong Sohn +2
Yes. In this paper, we investigate strong lottery tickets in generative models, the subnetworks that achieve good generative performance without any weight update. Neural network p…
Similarity of Neural Architectures using Adversarial Attack Transferability
Jaehui Hwang, Dongyoon Han, Byeongho Heo +3
In recent years, many deep neural architectures have been developed for image classification. Whether they are similar or dissimilar and what factors contribute to their (dis)simil…
Scratching Visual Transformer's Back with Uniform Attention
Nam Hyeon-Woo, Kim Yu-Ji, Byeongho Heo +3
The favorable performance of Vision Transformers (ViTs) is often attributed to the multi-head self-attention (MSA). The MSA enables global interactions at each layer of a ViT model…
Time Is MattEr: Temporal Self-supervision for Video Transformers
Sukmin Yun, Jaehyung Kim, Dongyoon Han +3
Understanding temporal dynamics of video is an essential aspect of learning better video representations. Recently, transformer-based architectural designs have been extensively ex…
Loss-based Sequential Learning for Active Domain Adaptation
Kyeongtak Han, Youngeun Kim, Dongyoon Han +1
Active domain adaptation (ADA) studies have mainly addressed query selection while following existing domain adaptation strategies. However, we argue that it is critical to conside…
An Extendable, Efficient and Effective Transformer-based Object Detector
Hwanjun Song, Deqing Sun, Sanghyuk Chun +5
Transformers have been widely used in numerous vision problems especially for visual recognition and detection. Detection transformers are the first fully end-to-end learning syste…
Frequency Selective Augmentation for Video Representation Learning
Jinhyung Kim, Taeoh Kim, Minho Shim +3
Recent self-supervised video representation learning methods focus on maximizing the similarity between multiple augmented views from the same video and largely rely on the quality…
Demystifying the Neural Tangent Kernel from a Practical Perspective: Can it be trusted for Neural Architecture Search without training?
Jisoo Mok, Byunggook Na, Ji-Hoon Kim +2
In Neural Architecture Search (NAS), reducing the cost of architecture evaluation remains one of the most crucial challenges. Among a plethora of efforts to bypass training of each…
Learning Features with Parameter-Free Layers
Dongyoon Han, YoungJoon Yoo, Beomyoung Kim +1
Trainable layers such as convolutional building blocks are the standard network design choices by learning parameters to capture the global context through successive spatial opera…