相对于triplet(Google FaceNet: A Unified Embedding for Face Recognition and Clustering:2015)和contrastive(汤晓鸥 Deep LearningFace Representation by Joint Identification-Verification:2014)来说,这个目标其实相对‘清晰’, 所以网络收敛的速度甚至比仅仅用softmax更快,而且不需要像前两者那样构造大量的训练对。看一...
Figure . (左) 基于 Softmax Loss 的特征; (右) 基于 Center Loss 的特征. Center Loss 是使得任意红色特征点之间的距离小于红色特征点与蓝色特征点之间的距离,以确保最好的不同特征类别的划分. 3.2 对比 ContrastIve Loss 和 Triplet Loss ContrastIve Loss 和 Triplet Loss 均是被用来提高深度特征的判别能力....
In fact, deep learning approaches for 3D object retrieval are quite similar to those for image or other object retrieval, where several loss functions such as contrastive loss [8] and triplet loss [29] have been introduced for training CNN, in order to learn a metric or an embedding space ...
mjDelta / keras-center-loss-MNIST Star 10 Code Issues Pull requests keras implementation of A Discriminative Feature Learning Approach for Deep Face Recognition based on MNIST loss-functions center-loss keras-center-loss Updated Mar 1, 2019 Jupyter Notebook samtwl / Deep-Learning-Contrastive-...
Cross-Modal Center Loss for 3D Cross-Modal Retrieval Longlong Jing∗ Elahe Vahdani∗ Jiaxing Tan Yingli Tian The City University of New York Abstract Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing...
while the federated-learning method significantly underperforms. for example, the dice score of hetero-mm-dsl is 0.795, which is significantly higher than the 0.353 dice score of hetero-real-fedseg. in addition, hetero-mm-dsl can handle this challenging problem with a small performance loss of...
In addition, the sampling strategies commonly used usually result in the loss of object information, and holistic and detailed information is beneficial for object localization. To address these challenges, we propose a novel Multi-view unsupervised center Uncertainty 3D single object Tracker (MUT). ...
2.2. Deep Metric Learning Deep Metric Learning (DML) aims at mapping the raw data into the feature space such that the distance among embeddings of the same class is less than that of dissimilar identities with well-designed DCNN models and an appropriate loss function. The key ingredient is ...
Multi-Head Self-Attention-Enhanced Prototype Network with Contrastive–Center Loss for Few-Shot Relation Extraction by Jiangtao Ma 1,2, Jia Cheng 1, Yonggang Chen 3, Kunlin Li 1, Fan Zhang 4 and Zhanlei Shang 1,* 1 College of Computer and Communication Engineering, Zhengzhou Univers...
The ‘class centers’ are computed in a similar way to the center loss [19], introducing almost negligible additional parameters and calculations—meaning that it can be easily applied to existing models. In contrast to earlier inter-sample self-distillation techniques, our proposed contrastive self...