Sequence Metric Learning as Synchronization of Recurrent Neural Networks

1 INTRODUCTION . Metric learning aims at learning an essential component for numerous machine learning algorithms used for classification or clustering : a similarity . It has the benefit to be usable in weakly supervised settings where only equivalence constraints between samples are known ( Xing et al . ( 2003 ) ) , which allows for a large number of applications on various data types : from person re-identification ( Yang et al . ( 2018 ) ) , object tracking ( Bertinetto et al . ( 2016 ) ) and gesture recognition ( Berlemont et al . ( 2018 ) ) to sentence similarity computation ( Mueller & Thyagarajan ( 2016 ) ) . Among those applications , less attention has been given to design specific sequence metric learning algorithms , specifically with neural networks despite the simplicity of the siamese architecture ( Bromley et al . ( 1994 ) ) . One easy way to adapt existing approaches to sequential data is to learn representations through Sequence-to-Sequence models ( Sutskever et al . ( 2014 ) ) or Transformers ( Vaswani et al . ( 2017 ) ) . However , these models would be difficult to learn in a weakly supervised way for providing a similarity metric and further lose temporal dependency information inside the sequence and alignment information between sequences . On the contrary , Dynamic Time Warping ( DTW ) ( Sakoe & Chiba ( 1978 ) ) is a classical approach to measure distance between sequences and relies on aligning sequences . Its integration inside learning algorithms has been rendered difficult by its non-differentiability and its theoretical quadratic time complexity which badly suits the equivalence constraint framework and some associated more complex losses ( Oh Song et al. , 2016 ; Sohn , 2016 ; Yang et al. , 2018 ) . Recent works mitigate these drawbacks notably with virtual metric learning ( Perrot & Habrard , 2015 ; Su & Wu , 2019 ) and soft versions of DTW ( Cai et al. , 2019 ; Abid & Zou , 2018 ) . Therefore , we aim at designing a neural network architecture specifically adapted to sequence metric learning . Recurrent neural networks ( RNN ) have a temporal dynamic behavior which allows to study them as dynamical systems . We propose in this paper a new framework for sequence metric learning based on dynamical system synchronization theory . We propose to replace the concept of metric in a vector space by the concept of synchronization of trajectories in a state space . Instead of computing distances on input representations , we propose to measure how two dynamical systems , and precisely two RNNs , respond to input pairs in term of synchronization . The notion of coupling is crucial when trying to synchronize dynamical systems . We introduce a coupled version of the Gated Recurrent Unit ( GRU ) ( Cho et al . ( 2014 ) ) to implement coupling inside a siamese architecture . Our experimental evaluation shows that this modification provides an improvement over a classical Siamese GRU implementation . The paper is organized as follows : Section 2 outlines the state-of-the-art approaches in sequence metric learning , Section 3 describes our framework and our new siamese architecture , Section 4 shows our experimental results to assess the performances of our approach compared to the state of the art , and Section 5 presents our conclusions and perspectives . 2 RELATED WORK . Recurrent neural networks and dynamical system theory . A main property of RNN is to exhibit a dynamic behavior which enables them to learn temporal sequence correlations . This behavior can therefore be studied using dynamical system theory : an important result being that RNN can approximate any finite-time trajectory of a dynamical system ( Funahashi & Nakamura ( 1993 ) ) . Other early works analyzed the RNN convergence stability ( Hirsch ( 1989 ) ) and helped to understand the problem of long term dependencies ( Bengio et al . ( 1994 ) ) . Laurent & von Brecht ( 2016 ) studied the dynamics of Long-Short Term Memory ( LSTM ) Neural Networks and GRU and observed that it is chaotic in the absence of input data . They designed a Chaos-Free RNN architecture having a more predictable behavior . In another recent publication , Chang et al . ( 2019 ) studied RNN trainability and established a connection with discretized Ordinary Differential Equations stability . They identified a criterion to guarantee that the system can preserve long-term dependencies and proposed a new version of RNN based on those observations . Both papers demonstrate that dynamical system theory is a fertile soil to study and conceive new RNN models . Finally , we would like to mention works on the definition of metrics to compare non-linear dynamical systems ( Martin ( 2000 ) ; Ishikawa et al . ( 2018 ) ) although our objective is not exactly the same , as we propose to use dynamical system synchronization theory to improve metric learning on any type of sequential data , whereas these methods have been conceived to work more specifically with structural data . Sequence metric learning . DTW is a classical approach for measuring distances between sequences ( Sakoe & Chiba ( 1978 ) ) . Numerous improvements have been brought to the original formulation notably to improve the k-nearest neighbor performance ( Xi et al . ( 2006 ) ) . Abid & Zou ( 2018 ) proposed to learn the DTW parameters that allow to reproduce the Euclidean distances between sequence representations learned with a Sequence-to-Sequence model ( Sutskever et al . ( 2014 ) ) . In contrast , Su & Hua ( 2017 ) proposed an alternative to DTW , the Order-Preserving Wasserstein ( OPW ) distance , by viewing the problem of metric learning between sequences as an optimal transport problem regularized to preserve the temporal relationships between the samples , and they solved it with the matrix scaling algorithm . Later ( Su & Wu ( 2019 ) ) , the authors reformulated the DTW and OPW distances as parameterized meta-metrics of a single ground metric and proposed an optimization process to learn the metric and the latent alignment with virtual metric learning ( Perrot & Habrard ( 2015 ) ) , which reduces the number of constraints . Not only this approach speeds up training but it also outperforms several other metric learning approaches , notably approaches conceived for points generalized to sequences . In comparison , we propose a pure RNN approach similar to Mueller & Thyagarajan ( 2016 ) who presented a siamese neural network approach to learn sentence similarities as a l1-norm . In their method , the LSTM network combines the embeddings of the words of the sentence to learn a distance between representations of sentences . Finally , Varior et al . ( 2016 ) proposed a siamese convolutional architecture for person re-identification from video data with gates linking parallel layers allowing to accentuate common patterns between both representations . This leads to representations that are more suited to distinguish some pairs of similar or dissimilar images . In this paper , we introduce an alternative to DTW : a pure neural network approach to sequence metric learning based on the siamese RNN architecture . We propose to enhance the classical Siamese RNN by studying this model from dynamical system point of view , as it has been already done for standard RNN . The disadvantage of DTW-based approaches compared to ours is that it can be computationally inefficient and non-differentiable , which prevents their possible combination with other gradient-based models . 3 SYNCHRONIZING GRU SIAMESE NETWORKS . In this section , we first draw a parallel between the concept of synchronization for dynamical systems and the task of sequence metric learning with siamese RNN . We then justify from a theoretical point of view the introduction of coupling inside the siamese architecture . We finally introduce our main contribution in Section 3.3 : a modified Siamese GRU model implementing this coupling . 3.1 SYNCHRONIZATION OF CHAOS AND METRIC LEARNING . The concept of synchronization is generally well-understood for time-periodic dynamical systems : this phenomenon is called phase synchronization . However , it is less-known that it can also occur for chaotic dynamical systems ( Pecora & Carroll ( 1990 ) ) , that is , systems for which resulting trajectories exponentially diverge for infinitesimally close initial conditions . This practically means that the behavior of such systems can become rapidly unpredictable solely due to small variations of the initial conditions . Common examples of such systems are the double pendulum or the n-body problem . To formalize the concept of synchronization for chaotic systems , Brown & Kocarev ( 2000 ) proposed a general definition of it : Definition 1 Let Z be a dynamical system composed of two subsystems X and Y such that : X : dx dt = f1 ( x , y ; t ) Y : dy dt = f2 ( y , x ; t ) , ( 1 ) where x ∈ Rd1 and y ∈ Rd2 with d1 , d2 ∈ N. Let φ ( z0 ) be a trajectory of Z with initial conditions z0 = [ x0 , y0 ] ∈ Rd1 × Rd2 . Finally let g : X ( resp . Y ) ×R → Rk with k ∈ N , be a measurable property of the subsystems . They are synchronized on the trajectory φ ( z0 ) with respect to the property g if there is a time independent function h : Rk × Rk → Rk such that : ||h ( g ( x ) , g ( y ) ) || = 0 , ( 2 ) where || · || is a norm . From this definition , it is possible to derive several ways to measure synchronization between two trajectories . Brown & Kocarev ( 2000 ) report several slightly different formulations of the synchronization error with the following being the most used . According to them , for identical systems : h ( g ( x ) , g ( y ) ) = lim t→+∞ ( g ( x ) − g ( y ) ) , ( 3 ) where h and g are the same as in Definition 1 . We rewrite this synchronization error for discrete systems in a continuous form by replacing the limit by a comparison of the last element of each trajectory X and Y of length T : h ( g ( X ) , g ( Y ) ) = XT − YT , ( 4 ) with g being here a function returning the coordinates of the points . We define d as a distance on discrete dynamical system trajectories derived from the synchronization error by replacing the difference with the Euclidean norm ( in definition 1 , any norm can be used ) to get only positive values : d ( X , Y ) = ||XT − YT ||2 . ( 5 ) However , RNNs are dynamical systems , and the output sequences are trajectories . Thus , learning a Euclidean distance with a siamese RNN is equivalent to trying to synchronize the output sequences of the two sub-networks of the siamese network for similar pairs . While being intuitive and suitable for metric learning , the metric of Equation 5 measures synchronization only at one point in time , which forces the system to achieve synchronization at this precise point . This is called dead-beat synchronization , synchronization in a finite number of steps ( De Angeli et al . ( 1995 ) ) . Even if at first sight , this seems not really different from computing distance on input sequence representations , synchronization could actually be assessed at several samples of the sequence and even continuously . We will now specify what type of synchronization siamese RNNs are able to achieve and under which conditions .

Summary: Drawing inspiration from dynamic systems, the paper proposes a novel architecture that couple sequences. Such a system has easiness to bring two instances arbitrarily close and authors have shown the superiority of the approach over an action recognition dataset; but the results seem to far from state of the art on the dataset (see questions section). The authors also recognize that currently such systems need to calculate each pairs (can't be cached due to coupling) at inference time, which is slow.