However, we hope that the learned feature should be discriminative and invariant to distortion. Hence, we designed a novel power activation function to accomplish the representation learning task. The nonsaturated ReLU [5], f(x) = max(0, x)f(x) = max(0, x), is widely utilized in the...
To address these problems, in this study, we propose a novel domain adaptation method, referred to as discriminative invariant alignment (DIA), for image representation. DIA enriches the knowledge matrix by combining the class discriminative information of the source domain and local data structure ...
At train stage, a nonlinear regression was learned on train data to infer class “template” in representation space using semantic embeddings as inputs. At test stage, as one test sample arrives, we first reduce its dimension to representation space, we calculate its similarities to unseen ...
This paper presents a discriminative scale invariant feature transform (D-SIFT) based feature representation for person-independent facial expression recognition. Keypoint descriptors of the SIFT features are used to construct distinctive facial feature vectors. Kullback Leibler divergence is used for the ...
Designing an appropriate feature representation and an effective matching framework for age invariant face recognition remains an open problem. In this paper, we propose a discriminative model to address face matching in the presence of age variation. In this framework, we first represent each face ...
DeepFirearm: Learning Discriminative Feature Representation for Fine-grained Firearm Retrieval INTRODUCTION 这是最近看的一篇文章,论文并没有太多方法上的创新点,但是将神经网络用于现代武器的检索,方向挺新奇的。所以记录一下。论文之初作者就指出现在CNN发展得很好,网络上像Facebook这一类...A...
To be specific, to achieve the domain-invariant discriminative features, TDFM jointly learns a shared encoding representation for two tasks: supervised classification of labeled source data, and discriminative clustering of unlabe...
Also, the convolutional pooling architecture is able to derive invariant and robust features. Therefore, the proposed method can learn robust and discriminative representation from the raw sensory data of induction motors in an efficient and automatic way. Finally, the learned representations are fed ...
(GP) model is proposed for sparse representation to optimize the dictionary objective function. Thesparse codingproperty allows a kernel with a compact support in GP to realize a very efficient dictionary learning process. Hence, an action video can be described by a set of compact and ...
As the interaction progresses, the dialog manager main- tains a representation of the state of the dialog in a process called dialog state tracking. For ex- ample, in a bus schedule information system, the dialog state might indicate the user's desired bus route, origin, and destination. ...