127,041 research outputs found
Transfer Learning for Improving Model Predictions in Highly Configurable Software
Modern software systems are built to be used in dynamic environments using
configuration capabilities to adapt to changes and external uncertainties. In a
self-adaptation context, we are often interested in reasoning about the
performance of the systems under different configurations. Usually, we learn a
black-box model based on real measurements to predict the performance of the
system given a specific configuration. However, as modern systems become more
complex, there are many configuration parameters that may interact and we end
up learning an exponentially large configuration space. Naturally, this does
not scale when relying on real measurements in the actual changing environment.
We propose a different solution: Instead of taking the measurements from the
real system, we learn the model using samples from other sources, such as
simulators that approximate performance of the real system at low cost. We
define a cost model that transform the traditional view of model learning into
a multi-objective problem that not only takes into account model accuracy but
also measurements effort as well. We evaluate our cost-aware transfer learning
solution using real-world configurable software including (i) a robotic system,
(ii) 3 different stream processing applications, and (iii) a NoSQL database
system. The experimental results demonstrate that our approach can achieve (a)
a high prediction accuracy, as well as (b) a high model reliability.Comment: To be published in the proceedings of the 12th International
Symposium on Software Engineering for Adaptive and Self-Managing Systems
(SEAMS'17
Hidden Two-Stream Convolutional Networks for Action Recognition
Analyzing videos of human actions involves understanding the temporal
relationships among video frames. State-of-the-art action recognition
approaches rely on traditional optical flow estimation methods to pre-compute
motion information for CNNs. Such a two-stage approach is computationally
expensive, storage demanding, and not end-to-end trainable. In this paper, we
present a novel CNN architecture that implicitly captures motion information
between adjacent frames. We name our approach hidden two-stream CNNs because it
only takes raw video frames as input and directly predicts action classes
without explicitly computing optical flow. Our end-to-end approach is 10x
faster than its two-stage baseline. Experimental results on four challenging
action recognition datasets: UCF101, HMDB51, THUMOS14 and ActivityNet v1.2 show
that our approach significantly outperforms the previous best real-time
approaches.Comment: Accepted at ACCV 2018, camera ready. Code available at
https://github.com/bryanyzhu/Hidden-Two-Strea
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