3498 papers with code • 1 benchmarks • 3 datasets
TensorFlow is an interface for expressing machine learning algorithms, and an implementation for executing such algorithms.
We show that the convolution-free VATT outperforms state-of-the-art ConvNet-based architectures in the downstream tasks.
Ranked #1 on Action Classification on Moments in Time (using extra training data)
We propose SpineNet, a backbone with scale-permuted intermediate features and cross-scale connections that is learned on an object detection task by Neural Architecture Search.
Ranked #4 on Image Classification on iNaturalist
The Machine Recognition of Crystallization Outcomes (MARCO) initiative has assembled roughly half a million annotated images of macromolecular crystallization experiments from various sources and setups.
The rising popularity of intelligent mobile devices and the daunting computational cost of deep learning-based models call for efficient and accurate on-device inference schemes.
We propose a new method for learning the structure of convolutional neural networks (CNNs) that is more efficient than recent state-of-the-art methods based on reinforcement learning and evolutionary algorithms.
Ranked #151 on Image Classification on ImageNet
Existing image classification datasets used in computer vision tend to have a uniform distribution of images across object categories.
Ranked #3 on Image Classification on iNaturalist
Categorical variables are a natural choice for representing discrete structure in the world.
Adversarial training provides a means of regularizing supervised learning algorithms while virtual adversarial training is able to extend supervised learning algorithms to the semi-supervised setting.
Ranked #13 on Sentiment Analysis on IMDb