这个在tensorflow官方文档中关于tf.nn.weighted_cross_entropy_with_logits函数用法说明也有相似的描述,感兴趣的同学可以查看一下。下面就来看看训练流程中与base model 两点不同的地方,完整代码实现可以参见本文附录。 输入数据的改变 这里以构造tf.data.Dataset输入为例,既然是reweight,自然每个样本我们就必须指定其sampl...
The weighted cross-entropy loss is L=−n∑j=1˜wjlog(mj)Kn, where the weights ˜wj are normalized to sum to n instead of 1. Exponential loss "exponential" L=n∑j=1wjexp(−mj). Hinge loss "hinge" L=n∑j=1wjmax{0,1−mj}. Logit loss "logit" L=n∑j=1wjlog(1+exp...
The weighted cross-entropy loss is L=−n∑j=1˜wjlog(mj)Kn, where the weights˜wjare normalized to sum toninstead of 1. Exponential loss"exponential"L=n∑j=1wjexp(−mj). Hinge loss"hinge"L=n∑j=1wjmax{0,1−mj}.
Video Re-localization 9 3.4 Training We train our model using the weighted cross entropy loss. We generate a label vector for the reference video at each time step. For a reference video with a ground-truth segment [s, e], we assume 1 ≤ s ≤ e ≤ r. The time steps belonging to ...
In order to verify the effectiveness of STCnet as a kind of data enhancement method, we use a simple re-ID network with average temporal pooling and the cross- entropy loss. We employ the modified ResNet-50 as the backbone net- work. In order to capture temporal dependency, we ...
Specifically, we learn a re-weighted strategy from the triplet loss to balance the sample pairs. According to the objective values of triple loss, the hard triple units will be endowed with larger weights, while the less important triple units are de-emphasized or simply dropped. Thus, ADV-...
keywords: adaptive weighted triplet loss, hard-identity mining project page: http://vision.cs.duke.edu/DukeMTMC/ arxiv: https://arxiv.org/abs/1803.10859 Mask-guided Contrastive Attention Model for Person Re-Identification intro: CVPR 2018 keywords: MGCAM paper: http://openaccess.thecvf.com/co...
Finally, the output matrix of the attention module is obtained by adding the weighted matrix to the original input matrix in the channel superposition stage. Loss functions In this paper, we introduce two loss functions: Softmax cross-entropy loss19 and hard mining triplet loss30. The total ...
注意到这种更新机制可以是以一种迭代的方式进行的,即: 但是,作者发现这种迭代的方式进行的提升,效果有限。在进行特征更新之后,我们将该 feature 输入到线性分类器当中,以得到相似性得分,然后照样利用 Binary cross-entropy loss 进行训练。 == Done !!!
那么如何去学习权重W呢?就是通过self-supervision产生的L signal并借助cross entropy loss 进行学习: 每个g都有一个ground truth label L ,因此(5)式并不会将第i个region的g属于第j个region的概率(i!=j)包括在内;(5)式迫使region locator将ground truth label 为i 的g属于Ri的概率向1逼近,因此该loss可以很...