Search Results for author: Asaf Noy

Found 17 papers, 14 papers with code

End-to-End Audio Strikes Back: Boosting Augmentations Towards An Efficient Audio Classification Network

1 code implementation25 Apr 2022 Avi Gazneli, Gadi Zimerman, Tal Ridnik, Gilad Sharir, Asaf Noy

While efficient architectures and a plethora of augmentations for end-to-end image classification tasks have been suggested and heavily investigated, state-of-the-art techniques for audio classifications still rely on numerous representations of the audio signal together with large architectures, fine-tuned from large datasets.

 Ranked #1 on Environmental Sound Classification on UrbanSound8K (using extra training data)

Classification Environmental Sound Classification +2

Diverse Imagenet Models Transfer Better

no code implementations19 Apr 2022 Niv Nayman, Avram Golbert, Asaf Noy, Tan Ping, Lihi Zelnik-Manor

Encouraged by the recent transferability results of self-supervised models, we propose a method that combines self-supervised and supervised pretraining to generate models with both high diversity and high accuracy, and as a result high transferability.

Multi-Label Classification

Solving ImageNet: a Unified Scheme for Training any Backbone to Top Results

2 code implementations7 Apr 2022 Tal Ridnik, Hussam Lawen, Emanuel Ben-Baruch, Asaf Noy

The scheme, named USI (Unified Scheme for ImageNet), is based on knowledge distillation and modern tricks.

Knowledge Distillation

BINAS: Bilinear Interpretable Neural Architecture Search

1 code implementation24 Oct 2021 Niv Nayman, Yonathan Aflalo, Asaf Noy, Rong Jin, Lihi Zelnik-Manor

Practical use of neural networks often involves requirements on latency, energy and memory among others.

Neural Architecture Search

Multi-label Classification with Partial Annotations using Class-aware Selective Loss

1 code implementation CVPR 2022 Emanuel Ben-Baruch, Tal Ridnik, Itamar Friedman, Avi Ben-Cohen, Nadav Zamir, Asaf Noy, Lihi Zelnik-Manor

We propose to estimate the class distribution using a dedicated temporary model, and we show its improved efficiency over a naive estimation computed using the dataset's partial annotations.

Multi-Label Classification

PETA: Photo Albums Event Recognition using Transformers Attention

1 code implementation26 Sep 2021 Tamar Glaser, Emanuel Ben-Baruch, Gilad Sharir, Nadav Zamir, Asaf Noy, Lihi Zelnik-Manor

We address this gap with a tailor-made solution, combining the power of CNNs for image representation and transformers for album representation to perform global reasoning on image collection, offering a practical and efficient solution for photo albums event recognition.

An Image is Worth 16x16 Words, What is a Video Worth?

2 code implementations25 Mar 2021 Gilad Sharir, Asaf Noy, Lihi Zelnik-Manor

Methods that reach State of the Art (SotA) accuracy, usually make use of 3D convolution layers as a way to abstract the temporal information from video frames.

Ranked #18 on Action Recognition on UCF101 (using extra training data)

Action Classification Action Recognition

HardCoRe-NAS: Hard Constrained diffeRentiable Neural Architecture Search

2 code implementations23 Feb 2021 Niv Nayman, Yonathan Aflalo, Asaf Noy, Lihi Zelnik-Manor

Realistic use of neural networks often requires adhering to multiple constraints on latency, energy and memory among others.

Neural Architecture Search

A Convergence Theory Towards Practical Over-parameterized Deep Neural Networks

no code implementations12 Jan 2021 Asaf Noy, Yi Xu, Yonathan Aflalo, Lihi Zelnik-Manor, Rong Jin

We show that convergence to a global minimum is guaranteed for networks with widths quadratic in the sample size and linear in their depth at a time logarithmic in both.

WeMix: How to Better Utilize Data Augmentation

no code implementations3 Oct 2020 Yi Xu, Asaf Noy, Ming Lin, Qi Qian, Hao Li, Rong Jin

To this end, we develop two novel algorithms, termed "AugDrop" and "MixLoss", to correct the data bias in the data augmentation.

Data Augmentation

TResNet: High Performance GPU-Dedicated Architecture

3 code implementations30 Mar 2020 Tal Ridnik, Hussam Lawen, Asaf Noy, Emanuel Ben Baruch, Gilad Sharir, Itamar Friedman

In this work, we introduce a series of architecture modifications that aim to boost neural networks' accuracy, while retaining their GPU training and inference efficiency.

Ranked #5 on Fine-Grained Image Classification on Oxford 102 Flowers (using extra training data)

Fine-Grained Image Classification General Classification +3

Knapsack Pruning with Inner Distillation

1 code implementation19 Feb 2020 Yonathan Aflalo, Asaf Noy, Ming Lin, Itamar Friedman, Lihi Zelnik

Through this we produce compact architectures with the same FLOPs as EfficientNet-B0 and MobileNetV3 but with higher accuracy, by $1\%$ and $0. 3\%$ respectively on ImageNet, and faster runtime on GPU.

Knowledge Distillation Network Pruning +1

XNAS: Neural Architecture Search with Expert Advice

2 code implementations NeurIPS 2019 Niv Nayman, Asaf Noy, Tal Ridnik, Itamar Friedman, Rong Jin, Lihi Zelnik-Manor

This paper introduces a novel optimization method for differential neural architecture search, based on the theory of prediction with expert advice.

Image Classification Neural Architecture Search

ASAP: Architecture Search, Anneal and Prune

1 code implementation8 Apr 2019 Asaf Noy, Niv Nayman, Tal Ridnik, Nadav Zamir, Sivan Doveh, Itamar Friedman, Raja Giryes, Lihi Zelnik-Manor

In this paper, we propose a differentiable search space that allows the annealing of architecture weights, while gradually pruning inferior operations.

Neural Architecture Search

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