855 papers with code • 0 benchmarks • 51 datasets
In our implementation, we have designed a search space where a policy consists of many sub-policies, one of which is randomly chosen for each image in each mini-batch.
Ranked #5 on Fine-Grained Image Classification on Caltech-101
The posterior over Bayesian neural network (BNN) parameters is extremely high-dimensional and non-convex.
We propose a novel algorithm that relies on a weak form of supervision where the data is partitioned into sets according to certain inactive factors of variation.
Contrastive learning applied to self-supervised representation learning has seen a resurgence in recent years, leading to state of the art performance in the unsupervised training of deep image models.
Ranked #234 on Image Classification on ImageNet
On LibriSpeech, we achieve 6. 8% WER on test-other without the use of a language model, and 5. 8% WER with shallow fusion with a language model.
Ranked #1 on Speech Recognition on Hub5'00 SwitchBoard
Contrastive self-supervised learning (CSL) is an approach to learn useful representations by solving a pretext task that selects and compares anchor, negative and positive (APN) features from an unlabeled dataset.
Ranked #21 on Image Classification on STL-10
Vision Transformers (ViT) have been shown to attain highly competitive performance for a wide range of vision applications, such as image classification, object detection and semantic image segmentation.
We present ResMLP, an architecture built entirely upon multi-layer perceptrons for image classification.
Ranked #5 on Image Classification on ImageNet V2 (using extra training data)