Found inside – Page 353But the cosine similarity between pa and pc is lower than that between pa and pb ... cosine loss to measure the similarity between two intra-class features. Found inside – Page 181Soft hash codes concatenated codes Fake image features Cosine Similarity Matrix Improved soft hash codes Adversarial loss Images egam I r otareneG egam I ta ... Found insideTriplet Loss For the triplet loss [39, 40], three samples are considered in ... In face verification, it has been demonstrated that cosine similarity is ... Found insideThe combined loss function for training the network is given by: Lcos+xent ... the Hamming distance instead of cosine similarity to compare image features. Found inside – Page 346... to the classification loss or the verification subnet with a pairwise verification loss. Rather than using the Euclidean or cosine distance between a ... Found inside – Page 35of predefined size and calculate the cosine similarity between a question and all ... To resolve this inefficiency, we propose an alternative loss function, ... Found inside – Page 64For each sketch i in the mini-batch, we calculate its cosine similarity with ... and minimize the similarity score of the negative pair, the similarity loss ... Found inside – Page 584Similarly, we propose a new Reconstruction Loss (RL) based on cosine similarity, the Cosine Reconstruction Loss (CRL): CRL(S,S)ˆ = l − l−1∑ cos(S(t), ... Found inside – Page 181We can now define the dot product similarity loss LDP the cosine similarity loss LCos, the contrastive dot product loss LCDP and the contrastive cosine loss ... Found inside – Page 725(3) The cosine which is similarity used to further loss between encourage β1 and the β encoder 2 is given to by produce Lsim = similar 12 cos〈β(1),β(2)〉, ... Found inside – Page 305... signal strength, while cosine loss evaluates the size of the differences between classes according to cosine similarity between the two feature vectors. Found inside – Page 151For the same purpose, we propose a batch-similarity loss to explicitly minimize ... the similarity between each sample pair are calculated by cosine metric, ... Found inside – Page 770Histograms of cosine similarity of positive (blue), negative (red), self (green) pairs trained ... (a) Attention-based ensemble (ABE-8) using proposed loss, ... Found inside – Page 45In particular, it defines the loss function based on the cosine similarity between the score vector outputted by the scoring function f for query q, ... Found inside – Page 128The cosine margin-based loss adds discriminative constraints explicitly on ... At testing cosine distance and L2 distance figure out similarity between a 1 ... Found inside – Page 272This measures the Euclidean distance between two unit vectors, which is equivalent to the cosine similarity. A key limitation of this loss is the vanishing ... Found inside – Page 62So the word-level cross entropy loss can be defined as: loss1 1⁄4 À ∑T ... The cosine similarity between hn and S can be computed as: cosðhn,SÞ 1⁄4 hn hn S ... Found inside – Page 3913, the x-axis shows the cutoff value for cosine similarity below which a ... 4.3 Loss Function There are two kinds of loss function used in this article. Found inside – Page 787The second scheme proposed in [48] is to fine-tune the CNN with loss function based on cosine similarity. In this scheme, the CNN model is trained in an ... Found inside – Page 360By setting an appropriate threshold for cosine loss, the feature distance learned by the network can be less than the threshold. However, cosine similarity ... In this thesis, we propose Triangular Similarity Metric Learning (TSML) for automatically specifying a metric from data. Found inside – Page 187The Q-DSSM structure was optimized by minimizing the cosine similarity-based loss over the training and validation sets. Two different encoders were used to ... Found inside – Page 165CleanNet adopts a cosine similarity loss with margin to impose this constraint: 1 − cos(φq,φs c ) Lcos ⎧ (φ q,φcs,l) = ⎪ ⎪ ⎨ ⎪ ⎪ ⎩ ω(max(0,cos(φq,φs ... Found inside – Page 24... angular margin because the angular metric with cosine similarity is intrinsically more suitable to the softmax loss. During training of A-Softmax Loss, ... Found inside – Page 245similarity label lij ∈{1,−1} (i.e., lij =1 if xi and x j belong to the ... and the cosine similarity is small, then there will be a large loss of Eq. (1). Found inside – Page 204.3 Loss Function Calculation In the calculation of the loss function, we use Maldives, ... two batches of distance by cosine similarity calculation. Found inside – Page 6X* is the ideal loss and S(X*, A.P) (x = P B) is cosine similarity measure between the ideal loss A" and the loss A. P. A.P is the loss of deciding the ... Found inside – Page 220In order to solve these challenges, Min et al. proposed a cosine similaritybased single damage detection method [6]. The method estimates cosine similarity ... Found inside – Page 241We use the cosine similarity to calculate the similarity vector between the ... 4.4 Combined Model and Loss Function We connect the user history vector uh, ... Found inside – Page 218Cosine Similarity Loss Function. In order to quantify mismatches of early layers of MDN and I3D, we need to compute the distance of the two tensors in high ... Found inside – Page 260We introduce a regularization loss that is formed by the negative cosine distance between synthesized i-vector̂x and original i-vector x Lcosx = −Ex∼p(x) ... Found inside – Page 83Cosine similarity and equal error rate (EER) are used for back-end scoring ... The ranked weight loss (RAM-Softmax loss) achieves the lowest EER compared ... Found inside – Page 103Wang cosine et al. describe similarity for the per-instance pair (S triplet d , Dcited) and a low cosine loss (Wang et al. similarity for 2014). Found inside – Page 181In the training process of Word2Vec, cosine similarity is a basic measurement which ... and it is usually trained according to the Cross-Entropy loss [6]. Found inside – Page 486Here the three definitions of loss are based on cosine similarity, Euclidean distance and cross entropy respectively. After computing the loss between the ... In this book, you will learn different techniques in deep learning to accomplish tasks related to object classification, object detection, image segmentation, captioning, . Found inside – Page 148To train the network to learn gaze direction, we use a cosine similarity loss function modified according to [12] to allow uncertainty estimation. Found inside – Page 711[10] normalizes the weights by replacing the inner product with cosine similarity in softmax loss. [11] applies the L2 constraint on features to embed faces ... Found inside – Page 327Determining the cosine similarity measure then requires representing the words of the query and pages as weighted vectors in ... Figure 3 illustrates the indexing of page 1 containing the word " weight " and page 2 the words “ diet " and “ loss ... Found inside – Page 216The distance regression module takes standardized images (by shifting and ... with the loss based on the cosine similarity and local constraints (Sect. Found inside – Page 491One possible choice is using cosine similarity for calculating the partial loss. as monotonically increasing and partial loss LI The weights are adjusted ... Found inside – Page 297The cosine score between the test wtest and target wtarget i-vectors is ... with a convex loss function [6] by calculating the cosine similarity between the ... Found inside – Page 33L2 loss is used for the six coordinate locations and three distance ... included in the loss function as the cosine similarity between the two vector sets. Found inside – Page 278The function gives an output output zero when the distance similarity of the ... Y = 1 + 1 e−x (9) Cosine Similarity loss function improves or maximizes ... Found inside – Page 56To overcome this weakness, we add a semantic similarity loss to the ... hdTd − heT e (8) Semantic Relevance: Cosine similarity is typically used to ... Found inside – Page 180... the first step to the cosine similarity loss function [23], which makes the detection precision of the base classes decreases less, especially when the ... Found inside – Page 694Contrastive loss Contrastive loss differentiates images by similarity. ... a maximum of 0 in the case of cosine distance or regularised Euclidean distance. Found inside – Page 80The network is trained by minimizing a contrastive loss on positive (i.e. ... 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