[42] proposed two different versions of the supervised contrastive loss to show which one performs the best. An accuracy of 81.4% on the ImageNet dataset with ResNet-200 was achieved, in which the value is 0.8% higher than the best value recorded for this architecture. On the other ...
Supervised-contrastive loss (SCL) is an alternative to cross-entropy (CE) for classification tasks that makes use of similarities in the embedding space to allow for richer representations. In this work, we propose methods to engineer the geometry of these learnt feature embeddings by modifying the...
首先回顾一下 contrastive loss,针对的是同 batch内部的样本 Lself=−∑i∈Aexp(fi⋅fj/τ)∑k∈A∖iexp(fi⋅fk/τ) 考虑到有可能采样到同一类的样本,有 supervised contrastive loss Lsup=∑i∈I−1|P(i)|∑p∈Plogexp(fi⋅fp/τ)∑a∈A∖iexp(fi⋅fa/τ) 拓展到...
这也是为什么在这篇论文之前,Person Re-Identification的researcher普遍不看好triplet loss:因为当我们 run naive triplet training时,很快训练就会陷入停滞,而如果我们要挑选hard positive/negative,又往往很time consuming。并且我们很难清晰地去定义 "good" hard triplets:如果我们选择太"hard"的triplets,training又会unstab...
Particularly, we use a supervised contrastive loss that encourages embeddings of maxillary sinus volumes with and without anomaly to form two distinct clusters while the cross-entropy loss encourages the 3D CNN to maintain its discriminative ability. We report that optimising with both losses is ...
3.1. Contrastive learning pretraining Motivated by the success of self-supervised contrastive learning, we propose to maximize the information content at the input of the downstream DL reconstruction model by mutually enhancing information contained in different acceleration factors. To achieve this, we ...
DREAM: Decoupled Representation via Extraction Attention Module and Supervised Contrastive Learning for Cross-Domain Sequential Recommender A Multi-view Graph Contrastive Learning Framework for Cross-Domain Sequential Recommendation Exploring False Hard Negative Sample in Cross-Domain Recommendation Domain Disentangl...
Unified unsupervised and semi-supervised domain adaptation network for cross-scenario face anti-spoofing Pattern Recognit., 115 (2021), p. 107888 View PDFView articleView in ScopusGoogle Scholar [21] S. Fatemifar, S.R. Arashloo, M. Awais, J. Kittler Client-specific anomaly detection for face...
( Supervised Contrastive Learning) loss. Supervised comparative learning is based on comparative learning. It improves the shortcomings of the original comparative learning that there is only one Positive Anchor. It uses samples of the same type as positive samples and different types of samples as ...
不过其实相似性的定义是很灵活的,尤其是supervised contrastive learning之后,样本的population啊,label啊,其实都可以拿来作为相似度的定义,具体怎么去定义这个相似性是alignment阶段比较重要的问题; 其它的就是一些trick部分了,包括使用哪种loss function,怎么做sampling mining or 利用memory bank or 利用moco的思路 去增强...