Advances few-shot classification by treating it as supervised learning and proposing new training techniques.
problem Formulating the ability of humans to learn from limited data in machine learning.
method Formulated few-shot classification as a supervised learning problem and introduced multi-episode and cross-way training techniques.
result Proposed training strategies accelerate the training process without accuracy loss.
Accurate image classification given small amounts of labelled data (few-shot classification) remains an open problem in computer vision. In this work we examine how the known texture bias of Convolutional Neural Networks (CNNs) affects few-shot classification performance. Although texture bias can help in standard imag…
Bayesian method improves few-shot classification accuracy.
problem Few-shot classification with small labeled datasets.
method Gaussian process classifier with Pólya-Gamma augmentation and one-vs-each softmax.
result Improved accuracy and uncertainty quantification.
Paper accelerates Bayesian few-shot classification using mirror descent.
problem Non-conjugate inference in Bayesian few-shot classification.
method Integrates mirror descent-based variational inference into Gaussian process-based few-shot classification.
result Accelerated convergence and improved uncertainty quantification.
CosML combines domain-specific meta-learners for cross-domain few-shot classification.
problem Generalizing to unseen domains while meta-learning on multiple seen domains.
method CosML trains domain-specific meta-learners and combines their meta-parameters in the parameter space.
result CosML outperforms state-of-the-art methods and achieves strong cross-domain generalization.
Graph Prototypical Networks improve few-shot node classification on attributed networks.
problem Few-shot node classification in attributed networks with limited labeled instances.
method Graph Prototypical Networks (GPN) using meta-learning to extract meta-knowledge and identify informative labeled instances.
result GPN achieves superior performance in few-shot node classification.
Paper introduces negative margin loss for better few-shot classification accuracy.
problem Improving few-shot classification accuracy with metric learning.
method Introduces negative margin loss and analyzes its impact on feature discriminability.
result Negative margin loss outperforms regular softmax loss on few-shot classification benchmarks.
Paper improves few-shot classification accuracy using feature distribution preprocessing.
problem Challenges of few-shot classification due to limited labelled samples.
method Proposes a novel transfer-based method that preprocesses feature vectors to Gaussian-like distributions and uses optimal-transport inspired algorithms.
result Achieves state-of-the-art accuracy on standardized vision benchmarks.
Interpretable neural model for few-shot time-series classification.
problem Few-shot time-series classification challenges.
method Dual Prototypical Shapelet Networks (DPSN) framework.
result DPSN framework outperforms state-of-the-art methods, especially with limited data.
Enhances few-shot image classification using unlabelled examples.
problem Few-shot image classification with limited labeled data.
method Transductive meta-learning combining soft k-means clustering and neural feature extractor.
result State-of-the-art performance on Meta-Dataset, mini-ImageNet, and tiered-ImageNet benchmarks.
This paper tackles few-shot classification by improving GAN-based data augmentation.
problem Improving few-shot classification performance using GANs with limited data.
method Fine-tuning GANs for few-shot classification, addressing training and evaluation challenges.
result Semi-supervised fine-tuning is a more effective approach for few-shot classification with limited data.
Bayesian online meta-learning framework tackles catastrophic forgetting in few-shot classification.
problem Catastrophic forgetting in few-shot classification problems.
method Bayesian online learning, meta-learning, Laplace approximation, variational inference.
result Framework effectively achieves goal of overcoming catastrophic forgetting in few-shot classification.
Recent algorithms with state-of-the-art few-shot classification results start their procedure by computing data features output by a large pretrained model. In this paper we systematically investigate which models provide the best representations for a few-shot image classification task when pretrained on the Imagenet …
Method improves few-shot one-class classification.
problem Learning binary classifier with data from only one class.
method Modified MAML algorithm to learn initialization for few-shot OCC.
result Method leads to better results than classical approaches.
DeepBDC improves few-shot classification by measuring joint distributions of image features.
problem Few-shot classification with limited training data.
method DeepBDC method using deep learning and Brownian Distance Covariance.
result DeepBDC significantly outperforms existing methods on various benchmarks.
FROB model improves robustness and reliable confidence for few-shot OoD detection.
problem Challenges in few-shot classification and OoD detection due to limited samples and adversarial attacks.
method FROB model combines support boundary generation and few-shot Outlier Exposure (OE) for improved robustness and reliable confidence.
result FROB achieves generalization to unseen OoD and maintains robustness independent of few-shot number.
Framework for few-shot relation classification with minimal training data.
problem Few-shot relation classification with limited training data.
method Meta-learning framework that combines instance and support knowledge.
result Framework outperforms state-of-the-art results and achieves competitive performance with large training data.
URT layer improves few-shot image classification across diverse domains.
problem Few-shot image classification in multi-domain settings.
method Meta-learns to dynamically re-weight and compose domain-specific representations.
result Sets new state-of-the-art on Meta-Dataset.
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation of each sentence is first recognized by distant supervision methods, and then filtered by crowdworkers. We adapt the most recent state-of-th…
Previous work on adversarially robust neural networks for image classification requires large training sets and computationally expensive training procedures. On the other hand, few-shot learning methods are highly vulnerable to adversarial examples. The goal of our work is to produce networks which both perform well a…
ProtoTransfer learns from unlabeled data to classify unseen tasks.
problem Few-shot classification with limited labeled data.
method Self-supervised prototypical transfer learning.
result ProtoTransfer outperforms unsupervised meta-learning methods.
In the few-shot scenario, a learner must effectively generalize to unseen classes given a small support set of labeled examples. While a relatively large amount of research has gone into few-shot learning for image classification, little work has been done on few-shot video classification. In this work, we address the …
The existing event classification (EC) work primarily focuseson the traditional supervised learning setting in which models are unableto extract event mentions of new/unseen event types. Few-shot learninghas not been investigated in this area although it enables EC models toextend their operation to unobserved event ty…
Few-shot learning (FSL) aims to learn novel visual categories from very few samples, which is a challenging problem in real-world applications. Many methods of few-shot classification work well on general images to learn global representation. However, they can not deal with fine-grained categories well at the same tim…
Metric-based few-shot learning methods try to overcome the difficulty due to the lack of training examples by learning embedding to make comparison easy. We propose a novel algorithm to generate class representatives for few-shot classification tasks. As a probabilistic model for learned features of inputs, we consider…
Improved few-shot learning with lower-level neural network embeddings.
problem Limited data scenarios in few-shot learning.
method Graph-based meta-learning framework using hidden layer feature embeddings.
result Utilization of lower-level neural network embeddings improves classifier accuracy.
A new method improves few-shot learning by combining ProtoNet with LFD.
problem Few-shot learning struggles with high variance support sets.
method Combines ProtoNet with Local Fisher Discriminant Analysis.
result Superior classification accuracy on miniImageNet and tieredImageNet.
Few-shot classification algorithms can alleviate the data scarceness issue, which is vital in many real-world problems, by adopting models pre-trained from abundant data in other domains. However, the pre-training process was commonly unaware of the future adaptation to other concept classes. We disclose that a classic…
Task-adaptive clustering improves few-shot learning with unlabeled data.
problem Handling unseen tasks with limited labeled data.
method Task-conditioned clustering in a new projection space.
result State-of-the-art semi-supervised few-shot classification performance.
We study few-shot acoustic event detection (AED) in this paper. Few-shot learning enables detection of new events with very limited labeled data. Compared to other research areas like computer vision, few-shot learning for audio recognition has been under-studied. We formulate few-shot AED problem and explore different…
Meta-learning has received a tremendous recent attention as a possible approach for mimicking human intelligence, i.e., acquiring new knowledge and skills with little or even no demonstration. Most of the existing meta-learning methods are proposed to tackle few-shot learning problems such as image and text, in rather …
We propose regression networks for the problem of few-shot classification, where a classifier must generalize to new classes not seen in the training set, given only a small number of examples of each class. In high dimensional embedding spaces the direction of data generally contains richer information than magnitude.…
Few-shot classification (FSC) is challenging due to the scarcity of labeled training data (e.g. only one labeled data point per class). Meta-learning has shown to achieve promising results by learning to initialize a classification model for FSC. In this paper we propose a novel semi-supervised meta-learning method cal…
Adaptive-Step Graph Meta-Learner tackles few-shot graph classification with limited labeled data.
problem Few labeled graph data in bioinformatics and other applications.
method A novel framework combining a graph meta-learner and a step controller for robust and generalization.
result State-of-the-art results on several few-shot graph classification tasks.
Paper tackles cross-granularity few-shot learning with meta-embedder.
problem Few-shot learning with coarse labels and fine-grained testing.
method Meta-embedder that optimizes visual and semantic discrimination across coarse and fine classes.
result Meta-embedder achieves effective cross-granularity few-shot classification.
LSTMs show surprising few-shot learning ability, improving on MAML.
problem Few-shot learning with limited data.
method Revisited LSTM approach with Outer Product LSTM (OP-LSTM).
result OP-LSTM outperforms MAML on simple few-shot tasks, but not complex ones.
Fine-tuning a deep network trained with the standard cross-entropy loss is a strong baseline for few-shot learning. When fine-tuned transductively, this outperforms the current state-of-the-art on standard datasets such as Mini-ImageNet, Tiered-ImageNet, CIFAR-FS and FC-100 with the same hyper-parameters. The simplicit…
New PAC-Bayes meta-learning method improves few-shot learning accuracy and calibration.
problem Few-shot learning with limited data.
method PAC-Bayes framework extended to meta-learning, estimating task-specific posteriors.
result State-of-the-art calibration and classification results on benchmarks.
Meta-learning strategy improves few-shot classification performance.
problem Few-shot classification with deep neural networks struggles when labeled samples are limited.
method Proposes an easy-to-hard expert meta-training strategy to arrange training tasks based on task hardness.
result Meta-learners achieve better results with the proposed expert training strategy.
Few-shot DP image classification models need more data as privacy increases.
problem Few-shot DP image classification challenges in personalization and federated learning.
method Exhaustive experiments on various parameters affecting few-shot DP accuracy and vulnerability.
result Increasing shots per class is necessary for DP accuracy as privacy increases.
Study reveals attributes help in few-shot classification generalization.
problem Understanding what makes some novel classes easier to learn.
method Defined attributes to quantify concept relatedness, used supervised and self-supervised learning.
result Combining self-supervised pretraining with supervised finetuning improves generalization.
Proposes a method to evaluate meta-learning performance based on task similarity.
problem Meta-learning performance evaluation ignores task similarity, leading to biased results.
method Generative approach using Latent Dirichlet Allocation to analyze task similarity.
result The proposed method provides an insightful evaluation of meta-learning algorithms, matching common intuition.
Improves few-shot learning using multi-task representation learning theory.
problem Few-shot learning with limited data.
method Multi-task representation learning theory and spectral-based regularization.
result Improved performance of meta-learning methods through new spectral regularization.
FiT combines transfer and meta-learning for efficient few-shot image classification.
problem Few-shot image classification in personalized and federated learning settings.
method Combines transfer learning and meta-learning with fixed pretrained backbones and fine-tuned FiLM adapter layers.
result Achieves state-of-the-art accuracy on VTAB-1k benchmark with fewer than 1% of updateable parameters.
ChatGPT models classify financial texts with minimal training.
problem Few-shot text classification in finance with limited labels.
method In-context learning with GPT-3.5 and GPT-4, fine-tuning with SetFit.
result GPT models outperform fine-tuned models with fewer examples.
A new method improves few-shot image classification by updating top layers.
problem Few-shot image classification with limited data.
method Layer-wise adaptive updating (LWAU) for meta-learning.
result LWAU outperforms existing methods with a clear margin and learns more efficiently.
Paper proposes a new pipeline for few-shot classification using forget-update module and channel vector sequence.
problem Few-shot classification with limited support samples.
method Channel vector sequence construction module and forget-update module.
result Pipeline achieves state-of-the-art results on various datasets.
Graph filtering reduces intra-class noise for improved classification accuracy.
problem Noise in training data affects classifier performance.
method Graph filtering to connect similar samples within a class.
result Asymptotic reduction of intra-class variance while maintaining mean.