21 citations · 33 across the 5 of their papers we have counts for
5 papers
Knowledge Assembly: Semi-Supervised Multi-Task Learning from Multiple Datasets with Disjoint Labels
Federica Spinola, Philipp Benz, Minhyeong Yu +1
In real-world scenarios we often need to perform multiple tasks simultaneously. Multi-Task Learning (MTL) is an adequate method to do so, but usually requires datasets labeled for…
Privacy Safe Representation Learning via Frequency Filtering Encoder
Jonghu Jeong, Minyong Cho, Philipp Benz +4
Deep learning models are increasingly deployed in real-world applications. These models are often deployed on the server-side and receive user data in an information-rich represent…
Poster: On the Feasibility of Training Neural Networks with Visibly Watermarked Dataset
Sanghyun Hong, Tae-hoon Kim, Tudor Dumitraş +1
As there are increasing needs of sharing data for machine learning, there is growing attention for the owners of the data to claim the ownership. Visible watermarking has been an e…
Training with the Invisibles: Obfuscating Images to Share Safely for Learning Visual Recognition Models
Tae-hoon Kim, Dongmin Kang, Kari Pulli +1
High-performance visual recognition systems generally require a large collection of labeled images to train. The expensive data curation can be an obstacle for improving recognitio…
ScreenerNet: Learning Self-Paced Curriculum for Deep Neural Networks
Tae-Hoon Kim, Jonghyun Choi
We propose to learn a curriculum or a syllabus for supervised learning and deep reinforcement learning with deep neural networks by an attachable deep neural network, called Screen…