笔记:GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks 2 年前 路痴斯基关注对于多目标任务学习的损失函数: L(t)=Σwi(t)Li(t) ,由于不同的任务的损失函数的梯度值在尺度上的不同,导致训练过程中某些任务或者某个单独任务可能会占据梯度训练的主导地位,而其他任务的梯度...
模型数量 18 智能监控 模型数量 15 水平线估计 模型数量 6 使用「Adaptive Robust Loss(Adaptive Loss)」的项目 3 模型资源 1 项目文献 车辆重新识别 2021年 SOTA! ON VeRi-776 mAP 87.1 智能监控 2021年 SOTA! ON VeRi-776 mAP 87.1 ConvLSTM (Huber Loss, naive residual path) ...
首先回到adaptive robust loss的场景(或从MSE换到MAE的场景),使用robust系的 loss之所以会改善泛化,是因为我们知道训练数据中存在一些离群样本,且它们几乎不提供可泛化的信息(以下也简称为脏数据)。而MAE相对于MSE loss对离群点敏感度低,受其影响小,所以能够提升泛化效果。如果这些所谓的离群点并非随机出现的无可泛...
While the existence of adaptive loss of function is no longer seriously disputed, the assumed maladaptive nature of loss of function from early theories can persist in the language of population genetics such as in the continued use ofdeleteriousas a synonym forloss-of-function(Moyers et al.2018...
View publication In most machine learning training paradigms a fixed, often handcrafted, loss function is assumed to be a good proxy for an underlying evaluation metric. In this work we assess this assumption by meta-learning an adaptive loss function to directly optimize the evaluation metric. We...
Official PyTorch implementation ofMeta-Learning with Task-Adaptive Loss Function for Few-Shot Learning (ICCV2021 Oral) The code is based off the public code ofMAML++, where their reimplementation of MAML is used as the baseline. The code also includes the implementation ofALFA. ...
Physics-informed loss function. The framework employs a physics-informed loss as a soft constraint, which biases the surrogate predictions towards physically consistent solutions. In particular, the employed hybrid strategy, described in Section “Neural operators”, combines data from high-fidelity simula...
(0,1) to estimate the gap between the term of ∇Band the gradient term of bce loss.alpha: A coefficient of poly loss.eps: Term added to the denominator to improve numerical stability.Returns:Loss tensor"""prob=inputs.sigmoid()ce_loss=F.binary_cross_entropy_with_logits(inputs,targets,...
最近,我看到一篇由Jon Barron在CVPR 2019中提出的关于为机器学习问题开发一个鲁棒、自适应的损失函数的文章。本文是对 A General and Adaptive Robust Loss Function 一些必要概念的回顾,它还将包含一个简单回归问题上的损失函数的实现。 关于异常值和鲁棒的损失的问题 ...
域自适应损失(Domain Adaptive Loss) 根据文章Conditional Domain Adversarial Network (CDAN)额外加一个域鉴别器(domain discriminator D)来处理 source distribution 和target distribution 之间的 domain adversarial loss,使域对齐: 采用以下条件调节策略: 域区分损失(Domain Discriminative Loss) ...