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...sticity and diverse augmentations, a Jensen-Shannon Divergence consistency loss, and a formulation to mix multiple augmented images to achieve state-of-the ...ions are often layered to create a high diversity of augmented images. The loss is calculated using the Jensen-Shannon divergence method. ...

[[File:DBIAPAVUCNN figure 1.png]] [[File:DBIAPAVUCNN table 1.png]] ...

...spinal cord, impacting the patient's upper and lower motor autonomy in the loss of muscle control. Its origin is still unknown, though in some instances it [[File:Table 1.png|center]] ...

...an loss function <math>\mathcal{L}</math> which combines a classification loss term <math>\mathcal{L}_s</math> involving the labelled image embedding and The classification loss <math>\mathcal{L}_s</math> is defined as: ...

...noise corrected loss approach. Another work presented a robust non-convex loss, which is the special case in a family of robust losses. In the noise rate ...ces to the peer network. <math>R(T)</math> governs the percentage of small-loss instances to be used in updating the parameters of each network. ...

..., and instead uses the distances between pairs of embedding to calculate a loss. The important thing is no the exact value of the vector but how it relates ...at that pixel. This threshold is calculated by using binary cross-entropy loss on the final values in the heat map. Values with likelihoods greater than p ...

...oencoder and is a neural network consisting of an encoder, a decoder and a loss function. They can be used for image generation and reinforcement learning ...ted as an affine transformation of some constant, so the derivative of the loss can be taken with respect to both of these parameters. ...

[[File:c433li-1.png|300px|center]] ...\rightarrow Y</math> mapping inputs to outputs. To quantify performance, a loss function <math>\ell:Y \times Y \rightarrow \mathbb{R}</math> is provided, a ...

...nd target image can be measured by Gramian Matrix. The authors defined the loss function as the Gramian Matrix of the activations in different layers. Desp ...tes the feature map output caused by the white noise image. Therefore this loss function preserves the content of the reference image. The style is describ ...

[[File:teaching 1.PNG|600px|center|thumb|Figure 3(a): Web-based feedback collection interface]] ...ochs, then for the following $t = 1$ to $T$ epochs, they use cross entropy loss for the first $P − \lfloor\frac{t}{m}\rfloor$ phrases (where $P$ denotes th ...

...orresponding image and sentence pair then the final max-margin, structured loss is: [[File:Multimodal RNN Results Table 1.png]] ...

...e ${(v_{n}, t_{n}, , n=1,...N)}$ is the training data, $\Delta$ is the 0-1 loss, $v_{n}$ are the images, $t_{n}$ are the text descriptions of class y. The [[File:keypt 1.PNG]] ...

[[File:CoGAN-1.PNG]] ...CoGAN cross-domain transformation results, computed by using the Euclidean loss function and the L-BFGS optimization algorithm. Namely, the authors conclud ...

...sionality of the data, while preserving its information (or minimizing the loss of information). Information comes from variation. In other words, capturin ...the loss of information is inevitable. Through PCA, we try to reduce this loss and capture most of the features of data. ...

...n}c\textbf{w}\end{align}</math>, for any scalar <math>c</math>, so without loss of generality we assume that: <br> .../www.soe.ucsc.edu/classes/cmps290c/Spring09/lect/7/pap_slides.pdf matching loss] as compared to other types of activation functions. ...

Without loss of generality we assume <math>\frac{f(y)}{f(x)}\frac{q(x|y)}{q(y|x)} > 1</m [[File:IMP ex part 1.png|600px]] ...