A concise review of recent few-shot meta-learning methods.
problem Mimicking human fast adaptation to new concepts based on prior knowledge.
method Categorized into four branches based on technical characteristics.
result Current challenges and future prospects identified.
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…
New online few-shot learning model for context-aware recognition.
problem Few-shot learning in online, continuous settings with spatiotemporal context.
method Proposed new dataset and online versions of existing few-shot learning approaches.
result Contextual prototypical memory model improves performance.
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…
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.
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.
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.
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.
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.
LMs perform poorly in true few-shot learning without held-out examples.
problem Evaluating few-shot performance of language models without access to held-out examples.
method Evaluated two model selection criteria (cross-validation and minimum description length) for choosing LM prompts and hyperparameters in true few-shot learning.
result Selection criteria often prefer models that perform worse than random selection, suggesting overestimation of few-shot ability.
Few-shot learning algorithms aim to learn model parameters capable of adapting to unseen classes with the help of only a few labeled examples. A recent regularization technique - Manifold Mixup focuses on learning a general-purpose representation, robust to small changes in the data distribution. Since the goal of few-…
Novel GNN model tackles few-shot learning with improved performance.
problem Few-shot learning with GNN suffers from over-fitting and over-smoothing.
method Proposes Attentive GNN with triple-attention mechanism.
result Improves GNN performance for few-shot learning tasks.
Paper introduces privacy-preserving few-shot learning for images.
problem Privacy risk in few-shot learning systems.
method Discrete embedding vectors and one-way hash functions.
result Achieves computational pan privacy without storing embeddings.
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.
Paper defines benchmarks for learning new tasks sequentially.
problem Efficient evaluation of continual few-shot learning.
method Theoretical framework and flexible benchmarks.
result Introduction of SlimageNet64 for efficient evaluation.
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.
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.
Paper proposes LMM-PQS for cross-domain few-shot learning.
problem Cross-domain few-shot learning problem.
method Generates pseudo query images and fine-tunes feature extraction modules with a large margin mechanism.
result LMM-PQS outperforms baseline models in cross-domain few-shot learning.
The field of few-shot learning has been laboriously explored in the supervised setting, where per-class labels are available. On the other hand, the unsupervised few-shot learning setting, where no labels of any kind are required, has seen little investigation. We propose a method, named Assume, Augment and Learn or AA…
Unified RL meta-learning framework for few-shot optimization.
problem Few-shot learning optimization problems.
method Generic RL meta-learning framework that learns optimal optimization algorithms.
result Significantly improved performance on few-shot tasks.
TIM maximizes mutual information for few-shot learning, outperforming state-of-the-art methods.
problem Few-shot learning with limited labeled data.
method Transductive Information Maximization (TIM) with alternating-direction solver.
result Significant improvement in accuracy across various datasets and networks.
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…
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 …
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.
The successful application of deep learning to many visual recognition tasks relies heavily on the availability of a large amount of labeled data which is usually expensive to obtain. The few-shot learning problem has attracted increasing attention from researchers for building a robust model upon only a few labeled sa…
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.
New PAC-Bayesian bounds explain few-shot learning performance gaps.
problem Gap between PAC-Bayesian theory and practice in few-shot learning.
method Developed new PAC-Bayesian bounds for few-shot learning, derived MAML and Reptile from these bounds, and introduced a new PACMAML algorithm.
result PAC-Bayesian bounds explain performance of MAML and Reptile, and outperform existing algorithms.
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.
We propose to study the problem of few-shot learning with the prism of inference on a partially observed graphical model, constructed from a collection of input images whose label can be either observed or not. By assimilating generic message-passing inference algorithms with their neural-network counterparts, we defin…
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.
Improved few-shot learning with LSSVM and transductive modules.
problem Few-shot learning with limited data and samples.
method Introducing LSSVM as a base learner and transductive modules to enhance classification accuracy.
result FSLSTM achieves state-of-the-art performance on miniImageNet and CIFAR-FS benchmarks.
The key issue of few-shot learning is learning to generalize. This paper proposes a large margin principle to improve the generalization capacity of metric based methods for few-shot learning. To realize it, we develop a unified framework to learn a more discriminative metric space by augmenting the classification loss…
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.
Few-shot learning has become essential for producing models that generalize from few examples. In this work, we identify that metric scaling and metric task conditioning are important to improve the performance of few-shot algorithms. Our analysis reveals that simple metric scaling completely changes the nature of few-…
A framework for using auxiliary data to improve few-shot learning.
problem Few-shot learning with scarce labeled examples and abundant auxiliary data.
method Automatic pseudo-shot selection and masking module to adjust auxiliary features.
result Masking module improves accuracy by 4.68 and 6.03 percentage points.
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…
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 …
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.
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.
Adaptive optimal transport priors improve few-shot learning robustness.
problem Limited supervision and distribution shifts in few-shot learning.
method Prototype-Guided Distributionally Robust Optimization (PG-DRO) framework.
result PG-DRO achieves stronger robust generalization in few-shot scenarios.
New method improves few-shot learning with randomized SPSA.
problem Training classifiers on limited examples of new classes.
method Randomized stochastic approximation and prototypical networks.
result The proposed method outperforms original prototypical networks.
Self-augmentation improves deep networks for few-shot learning with minimal training data.
problem Improving deep networks' generalization to unseen classes with limited training examples.
method Self-augmentation using self-mix and self-distillation techniques, combined with regional dropout and local representation learning.
result The method outperforms state-of-the-art few-shot learning methods on prevalent benchmarks.
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…
Meta-ticket finds optimal sparse subnetworks for few-shot learning in randomly initialized neural networks.
problem Avoiding overfitting in few-shot learning for over-parameterized neural networks.
method Meta-learning approach to find optimal sparse subnetworks.
result Meta-ticket discovers sparse subnetworks that adapt to each task, achieving superior meta-generalization.
Meta Omnium benchmarks few-shot learning across diverse vision tasks.
problem Evaluating generalization of few-shot learning across multiple vision tasks.
method Introduction of Meta Omnium dataset and evaluation of meta-learning algorithms.
result Meta-learning algorithms can generalize across diverse vision 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.
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.
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.