In this paper, we introduce (i) the AdaTriplet loss -- an extension of TL whose gradients adapt to different difficulty levels of negative samples, and (ii) the AutoMargin method -- a technique to adjust hyperparameters of margin-based losses such as TL and our proposed loss dynamically. ...
这是Triplet Loss以及Large Margin Nearest Neighborhor 的损失函数的判别准则。 同时我们可以证明,当 \gamma_2>0 的时候,线性ANML模型是关于投影函数 L 的凸优化模型,而 Large margin nearest nieghbor 方法,最重要的性质就是其为凸优化模型。 因此,当 \gamma_1 \rightarrow -\infty , \gamma_2 \rightarrow...
The proposed model is trained via triplet loss function that is adjusted for learning feature embeddings in a way that minimizes the distance between embedding-pairs from the same subject and maximizes the distance with those from different subjects, with a margin. In particular, a triplet Siamese...
1(b), at this point, positive triplet (Obama, place_of_birth, Honolulu) and negative triplet (Obama, place_of_birth, Hilo) is a hard pair, and the model automatically generates small bup and large blow, thus obtaining a larger desired margin that facilitates their correct classification. ...
Our approach achieves state-of-the-art classification results on a number of fine-grained visual recognition datasets, surpassing the standard softmax classifier and outperforming triplet loss by a relative margin of 30-40%. In terms of computational performance, it alleviates training inefficiencies ...
andij∈EErepresent the physical link connecting nodesViandVjinGG. A digital twin element of the physical network can be delineated as a triplet(ωijbw,ωimem,ωiCPU), wherei,j∈VV, andij∈EE. Here,ωijbw,ωimem, andωiCPUcorrespondingly signify the bandwidth of linkijas well as the ...
3.1. Adaptive Kernel Interpolation As stated in Section 1, cropping modifies the composi- tion of the original image and causes the loss of some crit- ical aesthetics information. As a result, image cropping in- troduces somewhat label noises in the traini...
To this end, Adaptive Large Margin N-Pair loss (ALMN) is proposed to address the aforementioned issues. First, the class center is adopted as the anchor point to avoid the difficulty on anchor selection. Then instead of exploring hard example-mining strategy, we introduce the adaptive large ...
To this end, Adaptive Large Margin N-Pair loss (ALMN) is proposed to address the aforementioned issues. First, the class center is adopted as the anchor point to avoid the difficulty on anchor selection. Then instead of exploring hard example-mining strategy, we introduce the adaptive large ...
Existing WTSG methods mainly adopted anchor-based structure to generate moment candidates and trained the network with triplet loss between the positive and negative samples, and thus the network performance was seriously affected by the preset anchors and the loss margin. In this paper, we propose ...