Google Inc.

Fujifilm Corporation

Sergey Ioffe

Jay Yagnik

Corinna Cortes

Michele Mae Covell

Samy Bengio

Troy Chinen

Henry Allan Rowley

Vivek Kwatra

Matthias Grundmann

Mei Han

Gal Chechik

Gheorghe Postelnicu

Alexander Toshev

Matthew Nirvan Sharifi

Shumeet Baluja

Charles Joseph Rosenberg

Vincent Olivier Vanhoucke

Bo Wu

Christian Szegedy

Hui Fang

Qixing Huang

Alexandra Ivanna Hawkins

Dominik Roblek

Yushi Jing

Luc Vincent

Erik Murphy-Chutorian

David Marwood

Andrea Lynn Frome

Raymond W Smith

Dennis William Strelow

George Tzanetakis

Krzysztof Kulewski

Nemanja Petrovic

Sean O'Malley

Stephen Conor Holiday

Yichang Shih

Lance Williams

Yang Song

Shengyang Dai

Pascal Massimino

Yangli Hector Yee

Saurabh Singh

Christian Frueh

Thomas Leung

Jasmine Kent

Richard Tucker

King Hong Thomas Leung

Mohamed Aly

Yair Movshovitz-Attias

Olivier Bousquet

Henrik Rydgard

Yangqing Jia

Yunchao Gong

King Hong Leung

Dennis Strelow

A neural network system that includes: multiple subnetworks that includes: a first subnetwork including multiple first modules, each first module including: a pass-through convolutional layer configured to process the subnetwork input for the first subnetwork to generate a pass-through output; an average pooling stack of neural network layers that collectively processes the subnetwork input for the first subnetwork to generate an average pooling output; a first stack of convolutional neural network layers configured to collectively process the subnetwork input for the first subnetwork to generate a first stack output; a second stack of convolutional neural network layers that are configured to collectively process the subnetwork input for the first subnetwork to generate a second stack output; and a concatenation layer configured to concatenate the pass-through output, the average pooling output, the first stack output, and the second stack output to generate a first module output for the first module.

Image classification neural networks

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing inputs using a neural network system that includes a batch normalization layer. One of the methods includes receiving a respective first layer output for each training example in the batch; computing a plurality of normalization statistics for the batch from the first layer outputs; normalizing each component of each first layer output using the normalization statistics to generate a respective normalized layer output for each training example in the batch; generating a respective batch normalization layer output for each of the training examples from the normalized layer outputs; and providing the batch normalization layer output as an input to the second neural network layer.

Batch normalization layers

Methods, systems, and apparatus, including computer programs encoded on computer storage medium, for training a neural network, wherein the neural network is configured to receive an input data item and to process the input data item to generate a respective score for each label in a predetermined set of multiple labels. The method includes actions of obtaining a set of training data that includes a plurality of training items, wherein each training item is associated with a respective label from the predetermined set of multiple labels; and modifying the training data to generate regularizing training data, comprising: for each training item, determining whether to modify the label associated with the training item, and changing the label associated with the training item to a different label from the predetermined set of labels, and training the neural network on the regularizing data.

Regularizing machine learning models

Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for implementing a neural network. In one aspect, the neural network includes a batch renormalization layer between a first neural network layer and a second neural network layer. The first neural network layer generates first layer outputs having multiple components. The batch renormalization layer is configured to, during training of the neural network on a current batch of training examples, obtain respective current moving normalization statistics for each of the multiple components and determine respective affine transform parameters for each of the multiple components from the current moving normalization statistics. The batch renormalization layer receives a respective first layer output for each training example in the current batch and applies the affine transform to each component of a normalized layer output to generate a renormalized layer output for the training example.

Batch renormalization layers

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for processing images or features of images using an image classification system that includes a batch normalization layer. One of the systems includes a convolutional neural network configured to receive an input comprising an image or image features of the image and to generate a network output that includes respective scores for each object category in a set of object categories, the score for each object category representing a likelihood that that the image contains an image of an object belonging to the category, and the convolutional neural network comprising: a plurality of neural network layers, the plurality of neural network layers comprising a first convolutional neural network layer and a second neural network layer; and a batch normalization layer between the first convolutional neural network layer and the second neural network layer.

Image classification using batch normalization layers

Methods, systems, and media for summarizing a video with video thumbnails are provided. In some embodiments, the method comprises: receiving a plurality of video frames corresponding to the video and associated information associated with each of the plurality of video frames; extracting, for each of the plurality of video frames, a plurality of features; generating candidate clips that each includes at least a portion of the received video frames based on the extracted plurality of features and the associated information; calculating, for each candidate clip, a clip score based on the extracted plurality of features from the video frames associated with the candidate clip; calculating, between adjacent candidate clips, a transition score based at least in part on a comparison of video frame features between frames from the adjacent candidate clips; selecting a subset of the candidate clips based at least in part on the clip score and the transition score associated with each of the candidate clips; and automatically generating an animated video thumbnail corresponding to the video that includes a plurality of video frames selected from each of the subset of candidate clips.

Methods, systems, and media for generating a summarized video with video thumbnails

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for implementing long-short term memory cells with saturating gating functions. One of the systems includes a first Long Short-Term Memory (LSTM) cell, wherein the first LSTM cell is configured to, for each of the plurality of time steps, generate a new cell state and a new cell output by applying a plurality of gates to a current cell input, a current cell state, and a current cell output, each of the plurality of gates being configured to, for each of the plurality of time steps: receive a gate input vector, generate a respective intermediate gate output vector from the gate input, and apply a respective gating function to each component of the respective intermediate gate output vector, wherein the respective gating function for at least one of the plurality of gates is a saturating gating function.

Long short-term memory cells with saturating gating functions

Growing community of inventors

Mountain View, CA, United States of America

Sergey Ioffe