5,075 research outputs found
Data Dropout: Optimizing Training Data for Convolutional Neural Networks
Deep learning models learn to fit training data while they are highly
expected to generalize well to testing data. Most works aim at finding such
models by creatively designing architectures and fine-tuning parameters. To
adapt to particular tasks, hand-crafted information such as image prior has
also been incorporated into end-to-end learning. However, very little progress
has been made on investigating how an individual training sample will influence
the generalization ability of a model. In other words, to achieve high
generalization accuracy, do we really need all the samples in a training
dataset? In this paper, we demonstrate that deep learning models such as
convolutional neural networks may not favor all training samples, and
generalization accuracy can be further improved by dropping those unfavorable
samples. Specifically, the influence of removing a training sample is
quantifiable, and we propose a Two-Round Training approach, aiming to achieve
higher generalization accuracy. We locate unfavorable samples after the first
round of training, and then retrain the model from scratch with the reduced
training dataset in the second round. Since our approach is essentially
different from fine-tuning or further training, the computational cost should
not be a concern. Our extensive experimental results indicate that, with
identical settings, the proposed approach can boost performance of the
well-known networks on both high-level computer vision problems such as image
classification, and low-level vision problems such as image denoising
Data Detection and Code Channel Allocation for Frequency-Domain Spread ACO-OFDM Systems Over Indoor Diffuse Wireless Channels
Future optical wireless communication systems promise to provide high-speed data transmission in indoor diffuse environments. This paper considers frequency-domain spread asymmetrically clipped optical orthogonal frequency-division multiplexing (ACOOFDM) systems in indoor diffuse channels and aims to develop efficient data detection and code channel allocation schemes. By exploiting the frequency-domain spread concept, a linear multi-code detection scheme is proposed to maximize the signal to interference plus noise ratio (SINR) at the receiver. The achieved SINR and bit error ratio (BER) performance are analyzed. A computationally efficient code channel allocation algorithm is proposed to improve the BER performance of the frequency-domain spread ACO-OFDM system.
Numerical results show that the frequency-domain spread ACO-OFDM system outperforms conventional ACO-OFDM systems in indoor diffuse channels. Moreover, the proposed linear multi-code detection and code channel allocation algorithm can improve the performance of optical peak-to-average power ratio (PAPR
Segment Anything in Medical Images
Segment anything model (SAM) has revolutionized natural image segmentation,
but its performance on medical images is limited. This work presents MedSAM,
the first attempt at extending the success of SAM to medical images, with the
goal of creating a universal tool for the segmentation of various medical
targets. Specifically, we first curate a large-scale medical image dataset,
encompassing over 200,000 masks across 11 different modalities. Then, we
develop a simple fine-tuning method to adapt SAM to general medical image
segmentation. Comprehensive experiments on 21 3D segmentation tasks and 9 2D
segmentation tasks demonstrate that MedSAM outperforms the default SAM model
with an average Dice Similarity Coefficient (DSC) of 22.5% and 17.6% on 3D and
2D segmentation tasks, respectively. The code and trained model are publicly
available at \url{https://github.com/bowang-lab/MedSAM}
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