最后,我们使用KL散度来计算loss: \begin{aligned} \text {loss} &=K L \text {-divergence}\left(y^{(s)}, y^{(p)}\right) \\ &=\sum_{c}^{C} y_{c}^{(s)} \log \left(\frac{y_{c}^{(s)}}{y_{c}^{(p)}}\right) \end{aligned} \\ ...
,即模拟标签分布,simulated label distribution(SLD)。 最后,我们使用KL散度来计算loss: loss=KL-divergence(y(s),y(p))=C∑cy(s)clog(y(s)cy(p)c) 总体来说还是比较简单的,很好复现,其实也存在更优的模型结构,我们还在探究。 四、实验&结果分析 1. Benchmark数据集上的测试 我们使用了2个中文数据集和...
在训练阶段的每个阶段,在前向传递过程中,预测的PDD映射和真实PDD映射之间计算KL散度损失(Kullback-Leibler,KL divergence),并在反向传播步骤中最小化。KL散度假设ConvLSTM网络的输出,为PDFs形式,计算预测结果与真实值之间的相对熵。卷积滤波器(如图4中不同的颜色所示)学习每个PDD地图中的空间信息,而LSTM模拟每个过滤后...
prior = Normal(torch.zeros(latent_dim), torch.ones(latent_dim)) # 计算KL散度作为正则项 kl_divergence = torch.distributions.kl_divergence(Normal(z, torch.ones_like(z)), prior) # ELBO损失 elbo_loss = recon_loss + kl_divergence # 输出ELBO损失 print(f'ELBO loss: {elbo_loss}') 对于每种...
aThe distribution of the number of rings of molecules generated by PMDM.bThe ratio of the molecules which contain rings of different sizes.cThe KL divergence of the bond angles of generated molecules from models with the test set.dThe KL divergence of the dihedral angles of generated molecules...
whereωijis the weight of the edge(vi,vj), andWis the sum of edge weights. To ensure the first-order similarity of nodes, the Kullback–Leibler (KL) divergence is used to measure the similarity between the empirical and the probabilistic distribution. The objective function is defined as foll...
Our model loss consists of two parts, including the cross-entropy loss\(J_{xent}\)and the KL divergenceKL(q(z|x)||p(z)). The cross-entropy loss expects the smallest reconstruction error between the reconstructed vector y and the input seed x. The KL divergence constraint model samples ...
Previous knowledge distillation methods often transfer knowledge via minimizing the Kullback–Leibler (KL) divergence between the logits from the last layer of the teacher network and those from the last layer of the student network. Our method splits both the student and the teacher networks into ...
By maximizing the KL divergence loss, the generator can generate more representative data. 3. The last loss function is for the purpose of making the synthetic data conform with the batch normalization statistics[1]. Finally, combine all those three loss functions together to create a whole loss...
(predicted, x, reduction='sum') # KL divergence kl_div = -0.5 * torch.sum(1 + z_var - z_mu.pow(2) - z_var.exp()) return recon_loss + kl_div # Example of parameters input_dim = 784 # for MNIST data hidden_dim = 400 z_dim = 20 # Instantiate the VAE vae = VAE(input...