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Found 8 papers, 2 papers with code
Is ImageNet worth 1 video? Learning strong image encoders from 1 long unlabelled video
But are we making the best use of data?
Skip-Attention: Improving Vision Transformers by Paying Less Attention
This work aims to improve the efficiency of vision transformers (ViT).
Teach me how to Interpolate a Myriad of Embeddings
Finally, to address inconsistencies due to linear target interpolation, we introduce a self-distillation approach to generate and interpolate synthetic targets.
AlignMix: Improving representations by interpolating aligned features
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.
It Takes Two to Tango: Mixup for Deep Metric Learning
In this work, we aim to bridge this gap and improve representations using mixup, which is a powerful data augmentation approach interpolating two or more examples and corresponding target labels at a time.
Ranked #8 on
Metric Learning
on CUB-200-2011
(using extra training data)
AlignMixup: Improving Representations By Interpolating Aligned Features
Mixup is a powerful data augmentation method that interpolates between two or more examples in the input or feature space and between the corresponding target labels.
Ranked #1 on
Representation Learning
on CIFAR10
Enhancing Visual Representations for Efficient Object Recognition during Online Distillation
Exploiting this fact, we aim to reduce the computations of our framework by employing a binary student network (BSN) to learn the frequently occurring classes using the pseudo-labels generated by the teacher network (TN) on an unlabeled image stream.
Attention Guided Anomaly Localization in Images
Without the need of anomalous training images, we propose Convolutional Adversarial Variational autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a convolutional latent variable to preserve the spatial information.
Ranked #74 on
Anomaly Detection
on MVTec AD
(Segmentation AUROC metric)
