3 research outputs found
RDPD: Rich Data Helps Poor Data via Imitation
In many situations, we need to build and deploy separate models in related
environments with different data qualities. For example, an environment with
strong observation equipments (e.g., intensive care units) often provides
high-quality multi-modal data, which are acquired from multiple sensory devices
and have rich-feature representations. On the other hand, an environment with
poor observation equipment (e.g., at home) only provides low-quality, uni-modal
data with poor-feature representations. To deploy a competitive model in a
poor-data environment without requiring direct access to multi-modal data
acquired from a rich-data environment, this paper develops and presents a
knowledge distillation (KD) method (RDPD) to enhance a predictive model trained
on poor data using knowledge distilled from a high-complexity model trained on
rich, private data. We evaluated RDPD on three real-world datasets and shown
that its distilled model consistently outperformed all baselines across all
datasets, especially achieving the greatest performance improvement over a
model trained only on low-quality data by 24.56% on PR-AUC and 12.21% on
ROC-AUC, and over that of a state-of-the-art KD model by 5.91% on PR-AUC and
4.44% on ROC-AUC.Comment: Published in IJCAI 201
CHEER: Rich Model Helps Poor Model via Knowledge Infusion
There is a growing interest in applying deep learning (DL) to healthcare,
driven by the availability of data with multiple feature channels in rich-data
environments (e.g., intensive care units). However, in many other practical
situations, we can only access data with much fewer feature channels in a
poor-data environments (e.g., at home), which often results in predictive
models with poor performance. How can we boost the performance of models
learned from such poor-data environment by leveraging knowledge extracted from
existing models trained using rich data in a related environment? To address
this question, we develop a knowledge infusion framework named CHEER that can
succinctly summarize such rich model into transferable representations, which
can be incorporated into the poor model to improve its performance. The infused
model is analyzed theoretically and evaluated empirically on several datasets.
Our empirical results showed that CHEER outperformed baselines by 5.60% to
46.80% in terms of the macro-F1 score on multiple physiological datasets.Comment: Published in TKD
Small Sample Learning in Big Data Era
As a promising area in artificial intelligence, a new learning paradigm,
called Small Sample Learning (SSL), has been attracting prominent research
attention in the recent years. In this paper, we aim to present a survey to
comprehensively introduce the current techniques proposed on this topic.
Specifically, current SSL techniques can be mainly divided into two categories.
The first category of SSL approaches can be called "concept learning", which
emphasizes learning new concepts from only few related observations. The
purpose is mainly to simulate human learning behaviors like recognition,
generation, imagination, synthesis and analysis. The second category is called
"experience learning", which usually co-exists with the large sample learning
manner of conventional machine learning. This category mainly focuses on
learning with insufficient samples, and can also be called small data learning
in some literatures. More extensive surveys on both categories of SSL
techniques are introduced and some neuroscience evidences are provided to
clarify the rationality of the entire SSL regime, and the relationship with
human learning process. Some discussions on the main challenges and possible
future research directions along this line are also presented.Comment: 76 pages, 15 figures, survey of small sample learnin