Effective detection of shot boundaries is important for video summarization methods based on shot boundary detection. However, various gradual shot boundaries (such as, fade in, fade out, dissolve) pose a great challe...
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In order to ensure the stable, safe and efficient operation of the power system, it is need to continuously strengthen the inspection of transmission lines in the power system. The traditional manual patrol has a larg...
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In the quest for advancement in Deep learning techniques, many new principle directions emerged into the light, but this paper is dedicated to usage of computer vision in healthcare, majorly brain tumors. This paper c...
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In recent years, convolutional neural networks, or CNN have made it feasible to do text categorization with great accuracy. Zhang et al. used character-level decomposition and a CNN with reasonably deep layers to achi...
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Freezing of gait (FOG) presents a temporary impact on the movement of Parkinson39;s disease patients. This is associated with sudden falls leading to injuries. Preventive measures can be useful in tackling such unfo...
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This paper explores using the AIGC tool ChatGPT to enhance interdisciplinary teaching in colleges and universities. Interdisciplinary courses, crucial for nurturing well-rounded talents, face challenges such as teache...
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Short-term traffic flow prediction is the premise of making full use of intelligent transportation system and plays an important role in urban traffic planning. In this paper, a deep extreme learning machine model bas...
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Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new class...
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ISBN:
(数字)9783031217531
ISBN:
(纸本)9783031217524;9783031217531
Continual learning is a novel learning setup for an environment where data are introduced sequentially, and a model continually learns new tasks. However, the model forgets the learned knowledge as it learns new classes. There is an approach that keeps a few previous data, but this causes other problems such as overfitting and class imbalance. In this paper, we propose a method that retrains a network with generated representations from an estimated multivariate Gaussian distribution. The representations are the vectors coming from CNN that is trained using a gradient regularization to prevent a distribution shift, allowing the stored means and covariances to create realistic representations. The generated vectors contain every class seen so far, which helps preventing the forgetting. Our 6-fold cross-validation experiment shows that the proposed method outperforms the existing continual learning methods by 1.14%p and 4.60%p in CIFAR10 and CIFAR100, respectively. Moreover, we visualize the generated vectors using t-SNE to confirm the validity of multivariate Gaussian mixture to estimate the distribution of the data representations.
In recent times, there has been a notable surge in the exploration of studying human body movements through the utilization of inertial measurement units that can be worn. This trend stems from its substantial impact ...
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Ultra-wideband signals have been widely used in high-precision indoor positioning because of their large bandwidth, low delay and strong penetration. However, the complex indoor environment will hinder the propagation...
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