24.4k citations · 25.5k across the 8 of their papers we have counts for
9 papers
Sample what you cant compress
Vighnesh Birodkar, Gabriel Barcik, James Lyon +3
For learned image representations, basic autoencoders often produce blurry results. Reconstruction quality can be improved by incorporating additional penalties such as adversarial…
Weighted Ensemble Self-Supervised Learning
Yangjun Ruan, Saurabh Singh, Warren Morningstar +4
Ensembling has proven to be a powerful technique for boosting model performance, uncertainty estimation, and robustness in supervised learning. Advances in self-supervised learning…
Towards a Semantic Perceptual Image Metric
Troy Chinen, Johannes Ballé, Chunhui Gu +8
We present a full reference, perceptual image metric based on VGG-16, an artificial neural network trained on object classification. We fit the metric to a new database based on 14…
Batch Renormalization: Towards Reducing Minibatch Dependence in Batch-Normalized Models
Sergey Ioffe
Batch Normalization is quite effective at accelerating and improving the training of deep models. However, its effectiveness diminishes when the training minibatches are small, or…
No Fuss Distance Metric Learning using Proxies
Yair Movshovitz-Attias, Alexander Toshev, Thomas K. Leung +2
We address the problem of distance metric learning (DML), defined as learning a distance consistent with a notion of semantic similarity. Traditionally, for this problem supervisio…
Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke +1
Very deep convolutional networks have been central to the largest advances in image recognition performance in recent years. One example is the Inception architecture that has been…