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...
pythondeep-learningpytorchstsmnistmetric-learningspeaker-verificationtriplet-losssiamesesemantic-textual-similaritytripletsiamese-networkcenter-losscontrastive-lossarcfacesimilarity-learningcoco-loss UpdatedApr 8, 2020 Python mjDelta/keras-center-loss-MNIST
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 ...
Finally, we measure the synthetic image quality using the proposed distributed metric termed Dist-FID. We believe Dist-FID will be the replacement of FID32 when distributed learning is used for medical image generation. Results In this section, we conduct experiments to systematically and ...
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 ...
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...
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 ...
learning_rate and weight_decay were heuristically tuned until fine-tuning has become stable. Note that those parameters strongly depend on the Timit dataset and might be suboptimal for other speech datasets. For more explanations on other parameters, one can take a look at...
To appreciate the performance of the proposed loss, in this experiment, several SOTA metric learning loss functions are used for comparison. The results demonstrate the effectiveness of our method in terms of classification accuracy and F1-scores....
Finally, we measure the synthetic image quality using the proposed distributed metric termed Dist-FID. We believe Dist-FID will be the replacement of FID32 when distributed learning is used for medical image generation. Results In this section, we conduct experiments to systematically and ...