Search Results for author: Oncel Tuzel

Found 32 papers, 8 papers with code

NeuMan: Neural Human Radiance Field from a Single Video

no code implementations23 Mar 2022 Wei Jiang, Kwang Moo Yi, Golnoosh Samei, Oncel Tuzel, Anurag Ranjan

Photorealistic rendering and reposing of humans is important for enabling augmented reality experiences.

Forward Compatible Training for Large-Scale Embedding Retrieval Systems

no code implementations6 Dec 2021 Vivek Ramanujan, Pavan Kumar Anasosalu Vasu, Ali Farhadi, Oncel Tuzel, Hadi Pouransari

To avoid the cost of backfilling, BCT modifies training of the new model to make its representations compatible with those of the old model.

Representation Learning

Synt++: Utilizing Imperfect Synthetic Data to Improve Speech Recognition

no code implementations21 Oct 2021 Ting-yao Hu, Mohammadreza Armandpour, Ashish Shrivastava, Jen-Hao Rick Chang, Hema Koppula, Oncel Tuzel

With recent advances in speech synthesis, synthetic data is becoming a viable alternative to real data for training speech recognition models.

Automatic Speech Recognition Speech Synthesis

Data Incubation -- Synthesizing Missing Data for Handwriting Recognition

no code implementations13 Oct 2021 Jen-Hao Rick Chang, Martin Bresler, Youssouf Chherawala, Adrien Delaye, Thomas Deselaers, Ryan Dixon, Oncel Tuzel

We use the framework to optimize data synthesis and demonstrate significant improvement on handwriting recognition over a model trained on real data only.

Handwriting Recognition

Token Pooling in Vision Transformers

no code implementations8 Oct 2021 Dmitrii Marin, Jen-Hao Rick Chang, Anurag Ranjan, Anish Prabhu, Mohammad Rastegari, Oncel Tuzel

Token Pooling is a simple and effective operator that can benefit many architectures.

Style Equalization: Unsupervised Learning of Controllable Generative Sequence Models

no code implementations6 Oct 2021 Jen-Hao Rick Chang, Ashish Shrivastava, Hema Swetha Koppula, Xiaoshuai Zhang, Oncel Tuzel

In this paper, we tackle the training-inference mismatch encountered during unsupervised learning of controllable generative sequence models.

Instance-Level Task Parameters: A Robust Multi-task Weighting Framework

no code implementations11 Jun 2021 Pavan Kumar Anasosalu Vasu, Shreyas Saxena, Oncel Tuzel

When applied to datasets where one or more tasks can have noisy annotations, the proposed method learns to prioritize learning from clean labels for a given task, e. g. reducing surface estimation errors by up to 60%.

Depth Estimation Multi-Task Learning +2

Optimize what matters: Training DNN-HMM Keyword Spotting Model Using End Metric

no code implementations2 Nov 2020 Ashish Shrivastava, Arnav Kundu, Chandra Dhir, Devang Naik, Oncel Tuzel

The DNN, in prior methods, is trained independent of the HMM parameters to minimize the cross-entropy loss between the predicted and the ground-truth state probabilities.

Keyword Spotting

Subject-Aware Contrastive Learning for Biosignals

1 code implementation30 Jun 2020 Joseph Y. Cheng, Hanlin Goh, Kaan Dogrusoz, Oncel Tuzel, Erdrin Azemi

Datasets for biosignals, such as electroencephalogram (EEG) and electrocardiogram (ECG), often have noisy labels and have limited number of subjects (<100).

Anomaly Detection Contrastive Learning +5

Extracurricular Learning: Knowledge Transfer Beyond Empirical Distribution

no code implementations30 Jun 2020 Hadi Pouransari, Mojan Javaheripi, Vinay Sharma, Oncel Tuzel

We propose extracurricular learning, a novel knowledge distillation method, that bridges this gap by (1) modeling student and teacher output distributions; (2) sampling examples from an approximation to the underlying data distribution; and (3) matching student and teacher output distributions over this extended set including uncertain samples.

Knowledge Distillation Transfer Learning

Unsupervised Style and Content Separation by Minimizing Mutual Information for Speech Synthesis

no code implementations9 Mar 2020 Ting-yao Hu, Ashish Shrivastava, Oncel Tuzel, Chandra Dhir

We present a method to generate speech from input text and a style vector that is extracted from a reference speech signal in an unsupervised manner, i. e., no style annotation, such as speaker information, is required.

Speech Synthesis

Least squares binary quantization of neural networks

1 code implementation9 Jan 2020 Hadi Pouransari, Zhucheng Tu, Oncel Tuzel

We conduct experiments on the ImageNet dataset and show a reduced accuracy gap when using the proposed least squares quantization algorithms.

Quantization

Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum

1 code implementation NeurIPS 2019 Shreyas Saxena, Oncel Tuzel, Dennis Decoste

To the best of our knowledge, our work is the first curriculum learning method to show gains on large scale image classification and detection tasks.

14 General Classification +2

OPTIMAL BINARY QUANTIZATION FOR DEEP NEURAL NETWORKS

no code implementations25 Sep 2019 Hadi Pouransari, Oncel Tuzel

We conduct experiments on the ImageNet dataset and show a reduced accuracy gap when using the proposed optimal quantization algorithms.

Quantization

MVX-Net: Multimodal VoxelNet for 3D Object Detection

1 code implementation2 Apr 2019 Vishwanath A. Sindagi, Yin Zhou, Oncel Tuzel

Many recent works on 3D object detection have focused on designing neural network architectures that can consume point cloud data.

3D Object Detection

Nonlinear Conjugate Gradients For Scaling Synchronous Distributed DNN Training

1 code implementation7 Dec 2018 Saurabh Adya, Vinay Palakkode, Oncel Tuzel

In this work, we propose and evaluate the stochastic preconditioned nonlinear conjugate gradient algorithm for large scale DNN training tasks.

General Classification

Divide, Denoise, and Defend against Adversarial Attacks

no code implementations19 Feb 2018 Seyed-Mohsen Moosavi-Dezfooli, Ashish Shrivastava, Oncel Tuzel

Improving the robustness of neural networks against these attacks is important, especially for security-critical applications.

Denoising

VoxelNet: End-to-End Learning for Point Cloud Based 3D Object Detection

46 code implementations CVPR 2018 Yin Zhou, Oncel Tuzel

Accurate detection of objects in 3D point clouds is a central problem in many applications, such as autonomous navigation, housekeeping robots, and augmented/virtual reality.

3D Object Detection Autonomous Navigation +4

Attentional Network for Visual Object Detection

no code implementations6 Feb 2017 Kota Hara, Ming-Yu Liu, Oncel Tuzel, Amir-Massoud Farahmand

We propose augmenting deep neural networks with an attention mechanism for the visual object detection task.

Object Detection

Learning from Simulated and Unsupervised Images through Adversarial Training

8 code implementations CVPR 2017 Ashish Shrivastava, Tomas Pfister, Oncel Tuzel, Josh Susskind, Wenda Wang, Russ Webb

With recent progress in graphics, it has become more tractable to train models on synthetic images, potentially avoiding the need for expensive annotations.

Ranked #3 on Image-to-Image Translation on Cityscapes Labels-to-Photo (Per-class Accuracy metric)

Domain Adaptation Gaze Estimation +2

Gaussian Conditional Random Field Network for Semantic Segmentation

no code implementations CVPR 2016 Raviteja Vemulapalli, Oncel Tuzel, Ming-Yu Liu, Rama Chellapa

In contrast to the existing approaches that use discrete Conditional Random Field (CRF) models, we propose to use a Gaussian CRF model for the task of semantic segmentation.

Semantic Segmentation

Global-Local Face Upsampling Network

no code implementations23 Mar 2016 Oncel Tuzel, Yuichi Taguchi, John R. Hershey

In our deep network architecture the global and local constraints that define a face can be efficiently modeled and learned end-to-end using training data.

Face Hallucination Face Reconstruction +1

Robust Face Alignment Using a Mixture of Invariant Experts

no code implementations13 Nov 2015 Oncel Tuzel, Tim K. Marks, Salil Tambe

Face alignment is particularly challenging when there are large variations in pose (in-plane and out-of-plane rotations) and facial expression.

Face Alignment Robust Face Alignment

Layered Interpretation of Street View Images

no code implementations15 Jun 2015 Ming-Yu Liu, Shuoxin Lin, Srikumar Ramalingam, Oncel Tuzel

We propose a layered street view model to encode both depth and semantic information on street view images for autonomous driving.

Autonomous Driving Scene Labeling +1

Deep Hierarchical Parsing for Semantic Segmentation

no code implementations CVPR 2015 Abhishek Sharma, Oncel Tuzel, David W. Jacobs

We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function.

General Classification Scene Parsing +1

Efficient Upsampling of Natural Images

no code implementations28 Feb 2015 Chinmay Hegde, Oncel Tuzel, Fatih Porikli

1) For the edge layer, we use a nonparametric approach by constructing a dictionary of patches from a given image, and synthesize edge regions in a higher-resolution version of the image.

Recursive Context Propagation Network for Semantic Scene Labeling

no code implementations NeurIPS 2014 Abhishek Sharma, Oncel Tuzel, Ming-Yu Liu

Then a top-down propagation of the aggregated information takes place that enhances the contextual information of each local feature.

Scene Labeling

Joint Geodesic Upsampling of Depth Images

no code implementations CVPR 2013 Ming-Yu Liu, Oncel Tuzel, Yuichi Taguchi

We propose an algorithm utilizing geodesic distances to upsample a low resolution depth image using a registered high resolution color image.

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