78 citations · 186 across the 16 of their papers we have counts for
6 papers · 1 filter
Learning with Hierarchical Complement Objective
Hao-Yun Chen, Li-Huang Tsai, Shih-Chieh Chang +4
Label hierarchies widely exist in many vision-related problems, ranging from explicit label hierarchies existed in image classification to latent label hierarchies existed in seman…
Natural Adversarial Sentence Generation with Gradient-based Perturbation
Yu-Lun Hsieh, Minhao Cheng, Da-Cheng Juan +3
This work proposes a novel algorithm to generate natural language adversarial input for text classification models, in order to investigate the robustness of these models. It invol…
COCO-GAN: Generation by Parts via Conditional Coordinating
Chieh Hubert Lin, Chia-Che Chang, Yu-Sheng Chen +3
Humans can only interact with part of the surrounding environment due to biological restrictions. Therefore, we learn to reason the spatial relationships across a series of observa…
Complement Objective Training
Hao-Yun Chen, Pei-Hsin Wang, Chun-Hao Liu +5
Learning with a primary objective, such as softmax cross entropy for classification and sequence generation, has been the norm for training deep neural networks for years. Although…
Improving Adversarial Robustness via Guided Complement Entropy
Hao-Yun Chen, Jhao-Hong Liang, Shih-Chieh Chang +4
Adversarial robustness has emerged as an important topic in deep learning as carefully crafted attack samples can significantly disturb the performance of a model. Many recent meth…
Graph-RISE: Graph-Regularized Image Semantic Embedding
Da-Cheng Juan, Chun-Ta Lu, Zhen Li +7
Learning image representations to capture fine-grained semantics has been a challenging and important task enabling many applications such as image search and clustering. In this p…