Authors:
Shogo Takasaki
and
Shuichi Enokida
Affiliation:
Graduate School of Computer Science and Systems Engineering, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka-shi, Fukuoka, 820-8502, Japan
Keyword(s):
Deep Learning, Anticipating Accidents, Long Short-Term Memory, Evaluating Learning Potential.
Abstract:
Deploying deep learning models on small-scale computing devices necessitates considering computational resources. However, reducing the model size to accommodate these resources often results in a trade-off with accuracy. The iterative process of training and validating to optimize model size and accuracy can be inefficient. A potential solution to this dilemma is the extrapolation of learning curves, which evaluates a model’s potential based on initial learning curves. As a result, it is possible to efficiently search for a network that achieves a balance between accuracy and model size. Nonetheless, we posit that a more effective approach to analyzing the latent potential of training models is to focus on the internal state, rather than merely relying on the validation scores. In this vein, we propose a module dedicated to scrutinizing the network’s internal state, with the goal of automating the optimization of both accuracy and network size. Specifically, this paper delves into a
nalyzing the latent potential of the network by leveraging the internal state of the Long Short-Term Memory (LSTM) in a traffic accident prediction network.
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