Supervised image classification with tens to hundreds of labeled training examples.
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To address the issue, we propose a novel transfer learning approach based on meta-learning that can automatically learn what knowledge to transfer from the source network to where in the target network.
The proposed architecture recaptures discarded supervision signals by complementing object detection with an auxiliary task in the form of semantic segmentation without introducing the additional complexity of previously proposed two-stage detectors.
In this paper, we present Hitachi and Paderborn University's joint effort for automatic speech recognition (ASR) in a dinner party scenario.
While there is currently a lot of enthusiasm about "big data", useful data is usually "small" and expensive to acquire.
On VOC07 testbed for few-shot image classification tasks on ImageNet with transfer learning (Goyal et al., 2019), replacing the linear SVM currently used with a Convolutional NTK SVM consistently improves performance.
Deep neural networks trained using a softmax layer at the top and the cross-entropy loss are ubiquitous tools for image classification.
In this paper we challenge the common assumption that convolutional layers in modern CNNs are translation invariant.
Inspired by Transformer and pre-trained language models from natural language processing, SMILES Transformer learns molecular fingerprints through unsupervised pre-training of the sequence-to-sequence language model using a huge corpus of SMILES, a text representation system for molecules.