In this paper we propose novel reconstruction loss that combines pixel-wise and perceptual information. Its performance is experimentally evaluated using generative adversarial networks (GANs), and compared against autoencoders based on MSE, showing superior performance. Additionally, we present an ...
将不同的loss计算方式结合在一起,训练结果也会相应地不同。图三中展示了不同的loss组合方式所展现的不同的超分辨率效果,其中E指MSE,P指perceptual loss,A指Adversarial...pixel-wise上的一致,而是鼓励两幅图具有相似的特征。 对于函数Φ,这篇论文采用了预训练好的VGG-19网络,通过VGG-19网络来提取出图像的有效特...
. . N , and calculate the pixel-wise mean squared error (MSE) of the reconstruction as an objective function to train the network: Figure 3. The first-layer filters trained on ImageNet with an up- scaling factor of 3. The filters are sorted based on their variances. The other way to...
wise with a background model. This approach allows each macroblock to have a different quantization parameter, satisfying the requirements of both variable and fixed bit-rate applications. Additionally, a low-complexity technique for color comparison is used to obtain pixel-wise precision at a ...
one straightforward choice of the objective loss function is the mean squared error (MSE); however, this loss might not be suitable for our case, because a dominant portion of each proximity map has zero values and a plain MSE might lead to a trivial solution such that the predictions for ...
Training Results and Loss Visualization Upon execution of the filetrain.py, our model gets trained. Below is the epoch-wise training history of the model which will be fully available to you once you run the module. $ python train.py ...
Following MAE [6], our model is trained via the mean squared error (MSE) loss between the reconstructed image and the original image on masked patches 1 D Di=1(xi − yi)2 where D is the total number of reconstructed pixels in the FOV. 3.3. Gener...
It is a generalised approach to visualise the contributions of non-linear classifiers by a pixel-wise decomposition of each pixel's output prediction. Starting from the output layer, the algorithm assigns a relevance (importance score) to the target neuron equal to the neuron's output. At the ...
al. [7] employed a three layer with MSE loss. Kim et In summary, the main contributions of the paper include: al. [21] improved accuracy by increasing the depth to Characterization of the underspecified super resolution layers and learning only the residuals between the high res- problem...
loss.py options.py test_MSE-model_CompareWithLabel.py test_MSE-model_on_Synthesis_IID-or-NonIID_noise.py test_MSE_Model_on_SEGY-field-noisy-data.py test_NonIID_Model_on_SEGY-field-noisy-data.py test_NonIID_model_CompareWithLabel.py test_NonIID_model_on_Synthesis_IID-or-NonIID_...