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Found 9 papers, 3 papers with code
Incremental Few-Shot Meta-Learning via Indirect Discriminant Alignment
This process enables incrementally improving the model by processing multiple learning episodes, each representing a different learning task, even with few training examples.
Supervised Momentum Contrastive Learning for Few-Shot Classification
In this work we investigate the complementary roles of these two sources of information by combining instance-discriminative contrastive learning and supervised learning in a single framework called Supervised Momentum Contrastive learning (SUPMOCO).
Estimating informativeness of samples with Smooth Unique Information
We define a notion of information that an individual sample provides to the training of a neural network, and we specialize it to measure both how much a sample informs the final weights and how much it informs the function computed by the weights.
Incremental Meta-Learning via Indirect Discriminant Alignment
Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation by solving multiple few-shot tasks sampled from a large dataset and a testing phase, where the meta-learner leverages its learnt internal representation for a specific few-shot task involving classes which were not seen during the meta-training phase.
FineText: Text Classification via Attention-based Language Model Fine-tuning
Training deep neural networks from scratch on natural language processing (NLP) tasks requires significant amount of manually labeled text corpus and substantial time to converge, which usually cannot be satisfied by the customers.
MARTHE: Scheduling the Learning Rate Via Online Hypergradients
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization, aiming at good generalization.
Scheduling the Learning Rate Via Hypergradients: New Insights and a New Algorithm
We study the problem of fitting task-specific learning rate schedules from the perspective of hyperparameter optimization.
$d$-SNE: Domain Adaptation using Stochastic Neighborhood Embedding
Deep neural networks often require copious amount of labeled-data to train their scads of parameters.
