Orthogonal Projection Loss(正交投影损失) 深度神经网络在一系列分类任务上均取得了卓越的性能,其中出现了softmax交叉熵(CE)损失,成为事实上的目标函数。 CE丧失鼓励班级的特征与真实类别向量相比,投影得分更高否定的阶级。但是,这是一个相对的约束并没有明确强制将不同的类特征设置为分开的。通过观察CE损失中的地面...
Orthogonal LoRA缓解遗忘是指的多个领域的LoRA之间缓解,其实并不能缓解模型内部知识的遗忘,因为第一个LoRA训练的时候,就会导致模型内部知识遗忘,那有没有什么方法可以让第一个LoRA训练的时候使得模型不遗忘呢,我提出Orthogonal Loss来解决这个问题。 得益于《Gradient Projection Memory for Continual Learning》和Orthogonal ...
Projections and projection matrices/operators play a crucial part in machine learning, signal processing, and optimization in general; after all, a projection corresponds to a minimization task when the loss is interpreted as a “distance.” Let A be an l×k,k<l, matrix with column vectors, ...
On Orthogonal Projections for Dimension Reduction and Applications in Augmented Target Loss Functions for Learning ProblemsOrthogonal ProjectionDimension reductionPreservation of data characteristicsSupervised learningTarget featuresThe use of orthogonal projections on high-dimensional input and target data in ...
14.Branch Loss Allocation Based on Circuit Theories and Orthogonal Projection基于电路理论与正交投影的支路损耗分摊方法 15.A New Method for Evaluating the Complexity of Enterprise Management Structure - Entropy Vector Projection;企业管理结构复杂度评价的新方法—熵正交投影法 ...
Thus the vector a ∙ (b ∧ c) may be thought of as a kind of projection of a into the subspace determined by the vectors b and c. However, it is not an orthogonal projection. To further examine this notion of projection, let unit vectors eˆ1 and eˆ2 be orthogonal. Thus ...
1 illustrates how the 30-point rule is modified for 10^{-4}<\gamma <10^2; in particular, we note that some nodes converge toward the projection of the poles of r(x;\gamma ) on the real axis as \gamma \rightarrow 0 and are dispersed by the appearance of roots of r(x;\gamma )...
However, standard temporal schemes, such as classical Runge-Kutta (RK) methods, do not enforce these constraints, leading to a loss of accuracy and stability. Projection is an efficient way to address this shortcoming by correcting the RK solution at the end of each time step. Here we ...
First, simi- lar to calculate the projection of a normal vector on orthog- onal basis, we can calculate projections (or so-called latent code) Z ∈ R1×(2n+1)2 of f (x, y) in Eq.(5) as follows: \label {eq11} Z[i]=\frac {1}{4}\int...
Orthogonal moments constitute statistical quantities derived by the projection of an image on an orthogonal polynomial basis. From: Applied Mathematics and Computation, 2010 About this pageSet alert Discover other topics On this page On this page Definition Chapters and Articles Related Terms Recommended...