Search Results for author: Aditya Ganeshan

Found 7 papers, 6 papers with code

Meta-learning Extractors for Music Source Separation

1 code implementation17 Feb 2020 David Samuel, Aditya Ganeshan, Jason Naradowsky

We propose a hierarchical meta-learning-inspired model for music source separation (Meta-TasNet) in which a generator model is used to predict the weights of individual extractor models.

Meta-Learning Music Source Separation

FDA: Feature Disruptive Attack

1 code implementation ICCV 2019 Aditya Ganeshan, B. S. Vivek, R. Venkatesh Babu

Though Deep Neural Networks (DNN) show excellent performance across various computer vision tasks, several works show their vulnerability to adversarial samples, i. e., image samples with imperceptible noise engineered to manipulate the network's prediction.

Adversarial Attack Image Classification

Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence

2 code implementations3 Sep 2018 Jogendra Nath Kundu, Rahul M. V., Aditya Ganeshan, R. Venkatesh Babu

In this work, we propose a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation.

Pose Estimation Viewpoint Estimation

iSPA-Net: Iterative Semantic Pose Alignment Network

2 code implementations3 Aug 2018 Jogendra Nath Kundu, Aditya Ganeshan, Rahul M. V., Aditya Prakash, R. Venkatesh Babu

Such image comparison based approach also alleviates the problem of data scarcity and hence enhances scalability of the proposed approach for novel object categories with minimal annotation.

Pose Estimation Semantic correspondence +1

Generalizable Data-free Objective for Crafting Universal Adversarial Perturbations

2 code implementations24 Jan 2018 Konda Reddy Mopuri, Aditya Ganeshan, R. Venkatesh Babu

Further, via exploiting simple priors related to the data distribution, our objective remarkably boosts the fooling ability of the crafted perturbations.

Adversarial Attack Depth Estimation +2

Per-Pixel Feedback for improving Semantic Segmentation

1 code implementation7 Dec 2017 Aditya Ganeshan

To understand how DCNN based models work at the task of semantic segmentation, we try to analyze the DCNN models in semantic segmentation.

Semantic Segmentation

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