为理解这些问题,本文使用高分辨率的可视化方法来提供神经网络loss function的经验特征,并探索神经网络loss function的non-convex结构和可训练能力的关系,以及最小点的几何性质(如sharpness/flatness,及周围landscape)是如何影响泛化性能的。 本文提出了“filter normalization”方法让我们能比较两个训练中得到的极小点,然后利...
As one of the important research topics in machine learning, loss function plays an important role in the construction of machine learning algorithms and the improvement of their performance, which has been concerned and explored by many researchers. But it still has a big gap to summarize, analy...
为了方便,这里先把 l1、l2 都定义成 CE loss,那么在第一项,它表现的像 positive learning,因为它就是一个传统的 CE function,而在第二项,它像 negative learning,也就是在标记错的时候,比如把狗标成汽车,如果用 positive learning 进行学习的话那就出现问题了,它是随机从一个 label 中进行抽取,希望让模型学...
deep-neural-networks deep-learning time-series dtw cuda pytorch dynamic-time-warping soft-dtw loss-function Updated Aug 3, 2021 Python PerdonLiu / CSE-Autoloss Star 56 Code Issues Pull requests Seach Losses of our paper 'Loss Function Discovery for Object Detection via Convergence-Simulation ...
LLM-ICL Principle：PART6之之重要paper标记 【不断勘误中，欢迎讨论指正】...Loss函数是用于衡量模型...
We compare the performance of several losses, and propose a novel, differentiable error function. We show that the quality of the results improves significantly with better loss functions, even when the network architecture is left unchanged.
Our empirical evidence suggests that the loss function must be smooth and have non-sparse gradients in order to work well with deep neural networks. Consequently, we introduce a family of smoothed loss functions that are suited to top-k optimization via deep learning. The widely used cross-...
In this paper, we propose a method of AutoML for loss function search named LFS-ReID in the framework of margin-based softmax loss function for person ReID. Specifically, we carefully design a sampling distribution based on the non-independent truncated Gaussian distributions to ensure that the ...
Cross-entropy, also known as logarithmic loss or log loss, is a popular loss function used in machine learning to measure the performance of a classification model. It measures the average number of bits required to identify an event from one probability distribution, p, using the optimal code...
AdaLFL (Online)0.0835±0.0050 Table 3:Averagerun-time of the entire learning process for each benchmark method. Each algorithm is run on a single Nvidia RTX A5000, and results are reported in hours. Task and ModelBaselineOfflineOnline