However, it introduces the scaling problem in latent space in a class of algorithms. We propose an extension to solve this problem by using scale invariance distance functions. The advantage of this extension is demonstrated for a particular case of joint-clustering with MSSC (minimizing sum-of-...
Reducing MNIST image data dimensionality by extracting the latent space representations of an Autoencoder model. Comparing these latent space representations to the default MNIST representation clusteringkerasmnistautoencodernearest-neighborsearth-movers-distance ...
2. Learning the Latent Space of Shapes 该章节则是学习形状隐式空间,即 c o d e code code,由于针对一个shape来学习一个网络是很不实用的,因此本文引入隐式向量 z z z 代表目标形状的隐式密码,此时如Fig. 3b所示:用3D点位置 x \boldsymbol{x} x 以及 隐式密码 z z z 作为输入,此处与上面同理,...
In this paper we present a long distance continuous language model based on a latent semantic analysis (LSA). In the LSA framework, the word-document co-occurrence matrix is commonly used to tell how many times a word occurs in a certain document. Also, the word-word co-occurrence matrix ...
However, many free-viewpoint video synthesis methods hardly satisfy the requirement to work in real time with high precision, particularly for sports fields having large areas and numerous moving objects. To address these issues, we propose a free-viewpoint video synthesis method based on distance ...
The Inception-v3 model used in FID is one in a library of modules introduced by Google as part of its GoogLeNet convolutional neural network in 2014. It was first discussed in aresearch papertitled "Going deeper with convolutions." These components transform raw imagery into a latent space for...
[4] proposed an encoder-recurrent-decoder model in which the recurrent layers incorporate nonlinear encoder and decoder networks, and the motion was predicted in the latent space. Martinez et al. [12] used a sequence-to-sequence architecture to predict the motion sequence. RNN methods have made...
In particular, we propose the use of sparse autoencoders that learn latent spaces that preserve the relative distance between inputs. Distance invariance between input and latent spaces allows our system to successfully learn compact representations that allow precise data reconstruction but also have ...
We present a novel approach that effectively identifies unknown objects by distinguishing between high and low-density regions in latent space. Our method builds upon the Open-Det (OD) framework, introducing two new elements to the loss function. These elements enhance the known embedding space's ...
aLet x and y be two points in a space of dimension n whose co-ordinates are x≡(x1; . . . xn)and y≡(y1; . . . yn), the lp distance between x and y is given by: 让x,并且y是二点在协调维度n的空间是x≡ (x1; . . . xn)和y≡ (y1; . . . 给yn), x之间的lp距离和...