Table 1. Effect of loss weighting on performance of ROI-AlignPS∗ (Yan et al., 2021b) and AlignPS (Yan et al., 2021a) when their loss of sub-task ti is manually assigned different weight wi (i=1,2,...,10). Method w1 w2 w3 w4 w5 w6 w7 w8 w9 w10 mAP Top-1 AlignPS -...
To address the above issues, an adaptive loss weighting multi-task model with attention-guide proposal generation is proposed. First, the proposed multi-task model can excavate contextual information to enrich the feature information of small-sized defect areas, enhancing the model's representation ...
(2) We design an adaptive loss weighting algorithm to weight the loss function of the four tasks, hoping to achieve the balance training of the four tasks by limiting the loss function to the same order of magnitude [23], [24], [25]. (3) Apply APINNs to solve solitary wave solutions...
7860 🧠 false mit demo/app.py DDMR: Deep Deformation Map Registration Learning deep abdominal CT registration through adaptive loss weighting and synthetic data generation DDMRwas developed by SINTEF Health Research. The corresponding manuscript describing the framework has been published inPLOS ONEand...
摘要: We regard PINNs as multitask learning.APINNs are physics-informed neural networks with an adaptive loss weighting algorithm.APINNs improve the training accuracy significantly.关键词: Physics-informed neural networks Deep learning Mukherjee-Kundu equation Sine-Gordon equation Benjamin-Ono equation ...
Adaptive Weighting Strategy Adaptive White Gaussian Noise Adaptive Wireless Path Protocol Adaptive Wireless System Adaptive zone Adaptive zone Adaptive zone Adaptive-Basis Function/Diagonal Moment Matrix Adaptive-Buffer Rate Control adaptive-control function ...
域区分损失(Domain Discriminative Loss) 为了在域对齐的情况下还能让域的类不对齐,引入了域区分损失。域判别损失被添加到特征提取CNN的输出中。这样可以区分具有自注意的嵌入层前后的特征,并将其混淆。 自适应重新加权模块(Adaptive Re-weighting Module)
F is the weighting factor, which represents the extent to which an individual obtains information regarding the location of the other individuals. It follows a Gaussian distribution with a mean of 0.5 and a variance of 0.3. Strategy IV: To increase the population diversity and prevent the ...
Visualizing the formation and aggregation of these fibrils within the brain has been limited by the notable resolution loss when imaging through tissues. With DL-AO adaptively optimizing single-molecule emission patterns during SMLM imaging, we can now clearly resolve the organization of ...
(optional) Weighting factor in range (0,1) to balancepositive vs negative examples. Default = -1 (no weighting).gamma: Exponent of the modulating factor (1 - p_t) tobalance easy vs hard examples.delta: A Factor in range (0,1) to estimate the gap between the term of ∇Band the ...