publications

2024

1. 

   Singular Values-Driven Automated Filter Pruning

   Van Tien Pham, Yassine Zniyed, and Thanh Phuong Nguyen

   2024

   Abs Video Code Website

   In this paper, we present SLIMING (Singular vaLues-drIven autoMated filter prunING), an automated filter pruning method that uses singular values to formalize the pruning process as an optimization problem over filter tensors. Recognizing that this original formulation poses a combinatorial challenge, we propose to replace it with a two-step process that consistently uses singular values in each phase: (i) determining the pruning configuration, which specifies the number of filters to retain in each layer, and (ii) selecting the filters themselves. We show that this approach ensures the preservation of the filters’ multidimensional structure throughout the pruning process. For each of these steps, we propose a straightforward algorithm to solve them. To validate each part of our approach, we performed a numerical simulation on an overparameterized synthetic toy example. Additionally, we conducted extensive simulations across eight architectures, four benchmark datasets, and four vision tasks, validating the efficacy of our framework.

2. 

   Efficient tensor decomposition-based filter pruning

   Van Tien Pham, Yassine Zniyed, and Thanh Phuong Nguyen

   Neural Networks, 2024

   Abs DOI Code

   "One model to compress them all, one approach to refine efficiency,
   One method to decompose tensors and enhance neural proficiency.
   In the realm of filters, CORING stands tall and true,
   Preserving dimensions, accuracy it will accrue.
   Experiments demonstrate its prowess, architectures put to test,
   FLOPS and parameters reduced, accuracy manifest.
   Like ResNet-50 in ImageNet’s vast domain,
   Memory and computation requirements it does restrain.
   Efficiency elevated, generalization takes its flight,
   In the world of neural networks, C💍RING shines its light."

3. 

   Enhanced Network Compression Through Tensor Decompositions and Pruning

   Van Tien Pham, Yassine Zniyed, and Thanh Phuong Nguyen

   IEEE Transactions on Neural Networks and Learning Systems, 2024

   Abs DOI Code

   Network compression techniques that combine tensor decompositions and pruning have shown promise in leveraging the advantages of both strategies. In this work, we propose enhanced Network cOmpRession through TensOr decompositions and pruNing (NORTON), a novel method for network compression. NORTON introduces the concept of filter decomposition, enabling a more detailed decomposition of the network while preserving the weight’s multidimensional properties. Our method incorporates a novel structured pruning approach, effectively integrating the decomposed model. Through extensive experiments on various architectures, benchmark datasets, and representative vision tasks, we demonstrate the usefulness of our method. NORTON achieves superior results compared to state-of-the-art (SOTA) techniques in terms of complexity and accuracy. Our code is also available for research purposes.

4. 

   Hybrid Network Compression through Tensor Decompositions and Pruning

   Van Tien Pham, Yassine Zniyed, and Thanh Phuong Nguyen

   In 2024 32nd European Signal Processing Conference (EUSIPCO), 2024

   Abs DOI Code

   The application of network compression methods, combining tensor decompositions and pruning, has demonstrated significant potential in harnessing the benefits of both strategies. This research introduces NORTON (hybrid Network cOmpression thRough Tensor decompOsitions and pruNing), a novel hybrid approach for network compression. The key innovation lies in the introduction of filter decomposition, enhancing the detailed breakdown of the network while preserving the multidimensional properties of filters. Our method incorporates a structured pruning approach, seamlessly integrating the decomposed model. Through a comprehensive set of experiments across various architectures, benchmark datasets, and representative vision tasks, the efficacy of our approach is highlighted.

2023

1. 

   Élagage efficace des filtres basé sur les décompositions tensorielles

   Van Tien Pham, Yassine Zniyed, and Thanh Phuong Nguyen

   In GRETSI 2023 XXIXème Colloque Francophone de Traitement du Signal et des Images, 2023

   Abs Code

   Nous présentons une nouvelle méthode d’élagage des filtres pour les réseaux de neurones, appelée CORING (pour effiCient tensOr decomposition-based filteR prunING en anglais). L’approche proposée maintient l’aspect multidimensionnel des filtres grâce à l’utilisation de décompositions tensorielles. Notre approche permet de mesurer la similarité entre les filtres de manière plus efficace et plus précise que les méthodes traditionnelles qui utilisent des versions vectorisées ou matricisées des filtres. Avec cette approche, nous pouvons effectuer l’élagage des filtres plus efficacement en gardant l’essentiel de l’information en utilisant des décompositions de tenseurs sur les filtres. Les expériences menées sur différentes architectures prouvent l’efficacité de CORING.

2021

1. 

   Detection and tracking hand from FPV: benchmarks and challenges on rehabilitation exercises dataset

   Van Tien Pham, Thanh Hai Tran, and Hai Vu

   In 2021 RIVF International Conference on Computing and Communication Technologies (RIVF), 2021

   Abs DOI Code

   Egocentric vision is an emerging field of computer vision characterized by the acquisition video from the first person perspective. Particularly, for evaluating upper extremity rehabilitation, egocentric vision offers the ability to quantitatively measure the function of hands used in physical-based exercises. For such applications, hand detection and tracking are the first requirement. In this work, we develop a fully automatic tracking by detection pipeline that firstly extracts hands positions and then tracks hands in consecutive frames. The proposed framework consists of state of the art detectors such as RCNN and YOLO family models coupled with advanced trackers (e.g., SORT and DeepSORT) for tracking task. This paper explores how performance of the stand alone object detection algorithms correlates with overall performance of a tracking by detection system. The experimental results show that detection highly impacts the overall performance. Moreover, this work also proves that the use of visual descriptors in the tracking stage can reduce the number of identity switches and thereby increase potential of the whole system. We also present challenges for new egocentric hand tracking dataset for future works.