Search Results for author: Vijay Chandrasekhar

Found 31 papers, 7 papers with code

Delving into Channels: Exploring Hyperparameter Space of Channel Bit Widths with Linear Complexity

no code implementations29 Sep 2021 Zhe Wang, Jie Lin, Xue Geng, Mohamed M. Sabry Aly, Vijay Chandrasekhar

We formulate the quantization of deep neural networks as a rate-distortion optimization problem, and present an ultra-fast algorithm to search the bit allocation of channels.

Quantization

A*HAR: A New Benchmark towards Semi-supervised learning for Class-imbalanced Human Activity Recognition

1 code implementation13 Jan 2021 Govind Narasimman, Kangkang Lu, Arun Raja, Chuan Sheng Foo, Mohamed Sabry Aly, Jie Lin, Vijay Chandrasekhar

Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised learning for HAR, particularly in a challenging scenario with class imbalance problem.

Human Activity Recognition

Learning to Prune Deep Neural Networks via Reinforcement Learning

no code implementations9 Jul 2020 Manas Gupta, Siddharth Aravindan, Aleksandra Kalisz, Vijay Chandrasekhar, Lin Jie

PuRL achieves more than 80% sparsity on the ResNet-50 model while retaining a Top-1 accuracy of 75. 37% on the ImageNet dataset.

Model Compression reinforcement-learning +1

Empirical Analysis of Overfitting and Mode Drop in GAN Training

no code implementations25 Jun 2020 Yasin Yazici, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Vijay Chandrasekhar

We examine two key questions in GAN training, namely overfitting and mode drop, from an empirical perspective.

MaskConvNet: Training Efficient ConvNets from Scratch via Budget-constrained Filter Pruning

no code implementations ICLR 2020 Raden Mu'az Mun'im, Jie Lin, Vijay Chandrasekhar, Koichi Shinoda

(4) Fast, it is observed that the number of training epochs required by MaskConvNet is close to training a baseline without pruning.

Network Pruning

Towards Effective 2-bit Quantization: Pareto-optimal Bit Allocation for Deep CNNs Compression

no code implementations25 Sep 2019 Zhe Wang, Jie Lin, Mohamed M. Sabry Aly, Sean I Young, Vijay Chandrasekhar, Bernd Girod

In this paper, we address an important problem of how to optimize the bit allocation of weights and activations for deep CNNs compression.

Quantization

A*3D Dataset: Towards Autonomous Driving in Challenging Environments

1 code implementation17 Sep 2019 Quang-Hieu Pham, Pierre Sevestre, Ramanpreet Singh Pahwa, Huijing Zhan, Chun Ho Pang, Yuda Chen, Armin Mustafa, Vijay Chandrasekhar, Jie Lin

With the increasing global popularity of self-driving cars, there is an immediate need for challenging real-world datasets for benchmarking and training various computer vision tasks such as 3D object detection.

3D Object Detection Autonomous Driving +4

Optimistic mirror descent in saddle-point problems: Going the extra(-gradient) mile

no code implementations ICLR 2019 Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.

Venn GAN: Discovering Commonalities and Particularities of Multiple Distributions

1 code implementation9 Feb 2019 Yasin Yazici, Bruno Lecouat, Chuan-Sheng Foo, Stefan Winkler, Kim-Hui Yap, Georgios Piliouras, Vijay Chandrasekhar

We propose a GAN design which models multiple distributions effectively and discovers their commonalities and particularities.

Dataflow-based Joint Quantization of Weights and Activations for Deep Neural Networks

no code implementations4 Jan 2019 Xue Geng, Jie Fu, Bin Zhao, Jie Lin, Mohamed M. Sabry Aly, Christopher Pal, Vijay Chandrasekhar

This paper addresses a challenging problem - how to reduce energy consumption without incurring performance drop when deploying deep neural networks (DNNs) at the inference stage.

Quantization

Holistic Multi-modal Memory Network for Movie Question Answering

no code implementations12 Nov 2018 Anran Wang, Anh Tuan Luu, Chuan-Sheng Foo, Hongyuan Zhu, Yi Tay, Vijay Chandrasekhar

In this paper, we present the Holistic Multi-modal Memory Network (HMMN) framework which fully considers the interactions between different input sources (multi-modal context, question) in each hop.

Question Answering Retrieval +1

Deep Adaptive Temporal Pooling for Activity Recognition

no code implementations22 Aug 2018 Sibo Song, Ngai-Man Cheung, Vijay Chandrasekhar, Bappaditya Mandal

Specifically, using frame-level features, DATP regresses importance of different temporal segments and generates weights for them.

Human Activity Recognition

Manifold regularization with GANs for semi-supervised learning

1 code implementation ICLR 2019 Bruno Lecouat, Chuan-Sheng Foo, Houssam Zenati, Vijay Chandrasekhar

Generative Adversarial Networks are powerful generative models that are able to model the manifold of natural images.

Optimistic mirror descent in saddle-point problems: Going the extra (gradient) mile

no code implementations7 Jul 2018 Panayotis Mertikopoulos, Bruno Lecouat, Houssam Zenati, Chuan-Sheng Foo, Vijay Chandrasekhar, Georgios Piliouras

Owing to their connection with generative adversarial networks (GANs), saddle-point problems have recently attracted considerable interest in machine learning and beyond.

Online Deep Learning: Growing RBM on the fly

no code implementations6 Mar 2018 Savitha Ramasamy, Kanagasabai Rajaraman, Pavitra Krishnaswamy, Vijay Chandrasekhar

The online generative training begins with zero neurons in the hidden layer, adds and updates the neurons to adapt to statistics of streaming data in a single pass unsupervised manner, resulting in a feature representation best suited to the data.

Binary Classification General Classification

End-to-End Video Classification with Knowledge Graphs

no code implementations6 Nov 2017 Fang Yuan, Zhe Wang, Jie Lin, Luis Fernando D'Haro, Kim Jung Jae, Zeng Zeng, Vijay Chandrasekhar

In particular, we unify traditional "knowledgeless" machine learning models and knowledge graphs in a novel end-to-end framework.

BIG-bench Machine Learning Classification +4

Pruning Convolutional Neural Networks for Image Instance Retrieval

no code implementations18 Jul 2017 Gaurav Manek, Jie Lin, Vijay Chandrasekhar, Ling-Yu Duan, Sateesh Giduthuri, Xiao-Li Li, Tomaso Poggio

In this work, we focus on the problem of image instance retrieval with deep descriptors extracted from pruned Convolutional Neural Networks (CNN).

Image Instance Retrieval Retrieval

Compact Descriptors for Video Analysis: the Emerging MPEG Standard

no code implementations26 Apr 2017 Ling-Yu Duan, Vijay Chandrasekhar, Shiqi Wang, Yihang Lou, Jie Lin, Yan Bai, Tiejun Huang, Alex ChiChung Kot, Wen Gao

This paper provides an overview of the on-going compact descriptors for video analysis standard (CDVA) from the ISO/IEC moving pictures experts group (MPEG).

Compression of Deep Neural Networks for Image Instance Retrieval

no code implementations18 Jan 2017 Vijay Chandrasekhar, Jie Lin, Qianli Liao, Olivier Morère, Antoine Veillard, Ling-Yu Duan, Tomaso Poggio

One major drawback of CNN-based {\it global descriptors} is that uncompressed deep neural network models require hundreds of megabytes of storage making them inconvenient to deploy in mobile applications or in custom hardware.

Image Instance Retrieval Model Compression +2

Nested Invariance Pooling and RBM Hashing for Image Instance Retrieval

no code implementations15 Mar 2016 Olivier Morère, Jie Lin, Antoine Veillard, Vijay Chandrasekhar, Tomaso Poggio

The first one is Nested Invariance Pooling (NIP), a method inspired from i-theory, a mathematical theory for computing group invariant transformations with feed-forward neural networks.

Image Instance Retrieval Retrieval +1

Egocentric Activity Recognition with Multimodal Fisher Vector

no code implementations25 Jan 2016 Sibo Song, Ngai-Man Cheung, Vijay Chandrasekhar, Bappaditya Mandal, Jie Lin

With the increasing availability of wearable devices, research on egocentric activity recognition has received much attention recently.

Egocentric Activity Recognition

Group Invariant Deep Representations for Image Instance Retrieval

no code implementations9 Jan 2016 Olivier Morère, Antoine Veillard, Jie Lin, Julie Petta, Vijay Chandrasekhar, Tomaso Poggio

Based on a thorough empirical evaluation using several publicly available datasets, we show that our method is able to significantly and consistently improve retrieval results every time a new type of invariance is incorporated.

Dimensionality Reduction Image Classification +3

Tiny Descriptors for Image Retrieval with Unsupervised Triplet Hashing

no code implementations10 Nov 2015 Jie Lin, Olivier Morère, Julie Petta, Vijay Chandrasekhar, Antoine Veillard

Then, triplet networks, a rank learning scheme based on weight sharing nets is used to fine-tune the binary embedding functions to retain as much as possible of the useful metric properties of the original space.

Image Classification Image Retrieval +1

Co-Regularized Deep Representations for Video Summarization

no code implementations30 Jan 2015 Olivier Morère, Hanlin Goh, Antoine Veillard, Vijay Chandrasekhar, Jie Lin

A comprehensive user study is conducted comparing our proposed method to a variety of schemes, including the summarization currently in use by one of the most popular video sharing websites.

Informativeness Video Summarization

DeepHash: Getting Regularization, Depth and Fine-Tuning Right

no code implementations20 Jan 2015 Jie Lin, Olivier Morere, Vijay Chandrasekhar, Antoine Veillard, Hanlin Goh

This work focuses on representing very high-dimensional global image descriptors using very compact 64-1024 bit binary hashes for instance retrieval.

Retrieval

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