Learning from a few examples is a challenging computer vision task. Traditionally,meta-learning-based methods have shown promise towards solving this problem.Recent approaches show benefits by learning a feature extractor on the abundantbase examples and transferring these to the fewer novel examples. However, thefinetuning stage is often prone to overfitting due to the small size of the noveldataset. To this end, we propose Few shot Learning with hard Mixup (FeLMi)using manifold mixup to synthetically generate samples that helps in mitigatingthe data scarcity issue. Different from a naïve mixup, our approach selects the hardmixup samples using an uncertainty-based criteria. To the best of our knowledge,we are the first to use hard-mixup for the few-shot learning problem.

Learning from a few examples remains a key challenge in machine learning. Despite recent advances in important domains such as vision and language, the standard supervised deep learning paradigm does not offer a satisfactory solution for learning new concepts rapidly from little data. In this work, we employ ideas from metric learning based on deep neural features and from recent advances that augment neural networks with external memories. Our framework learns a network that maps a small labelled support set and an unlabelled example to its label, obviating the need for fine-tuning to adapt to new class types. We then define one-shot learning problems on vision (using Omniglot, ImageNet) and language tasks. Our algorithm improves one-shot accuracy on ImageNet from 82.2% to 87.8% and from 88% accuracy to 95% accuracy on Omniglot compared to competing approaches. We also demonstrate the usefulness of the same model on language modeling by introducing a one-shot task on the Penn Treebank.

The remarkable representational power of Vision Transformers (ViTs) remains underutilized in few-shot image classification. In this work, we introduce ViT-ProtoNet, which integrates a ViT-Small backbone into the Prototypical Network framework. By averaging class conditional token embeddings from a handful of support examples, ViT-ProtoNet constructs robust prototypes that generalize to novel categories under 5-shot settings. We conduct an extensive empirical evaluation on four standard benchmarks: Mini-ImageNet, FC100, CUB-200, and CIFAR-FS, including overlapped support variants to assess robustness. Across all splits, ViT-ProtoNet consistently outperforms CNN-based prototypical counterparts, achieving up to a 3.2\% improvement in 5-shot accuracy and demonstrating superior feature separability in latent space. Furthermore, it outperforms or is competitive with transformer-based competitors using a more lightweight backbone. Comprehensive ablations examine the impact of transformer depth, patch size, and fine-tuning strategy. To foster reproducibility, we release code and pretrained weights. Our results establish ViT-ProtoNet as a powerful, flexible approach for few-shot classification and set a new baseline for transformer-based meta-learners.

Comments: the idea contained in the paper is not a very big contribution to the field but still remains interesting, and the experiments on different datasets provide important information concerning the behavior of the model. But the paper has many different problems that make it difficult to understand. The first problem is in the structure of the paper since the problem formulation is in fact only given in Section 2.2 while I think it is important to well define the one-shot learning problem at the beginning of the paper. It would make the article easier to read and to follow. Sections 2.1 and 2.1.1 are well written but could include more precise definitions.

Learning from a few examples is a challenging computer vision task. Traditionally,meta-learning-based methods have shown promise towards solving this problem.Recent approaches show benefits by learning a feature extractor on the abundantbase examples and transferring these to the fewer novel examples. However, thefinetuning stage is often prone to overfitting due to the small size of the noveldataset. To this end, we propose Few shot Learning with hard Mixup (FeLMi)using manifold mixup to synthetically generate samples that helps in mitigatingthe data scarcity issue. Different from a naïve mixup, our approach selects the hardmixup samples using an uncertainty-based criteria.

Traditional methods like Graph Convolutional Networks (GCNs) face challenges with limited data and class imbalance, leading to suboptimal performance in graph classification tasks during toxicity prediction of molecules as a whole. To address these issues, we harness the power of Graph Isomorphic Networks, Multi Headed Attention and Free Large-scale Adversarial Augmentation separately on Graphs for precisely capturing the structural data of molecules and their toxicological properties. Additionally, we incorporate Few-Shot Learning to improve the model's generalization with limited annotated samples. Extensive experiments on a diverse toxicology dataset demonstrate that our method achieves an impressive state-of-art AUC-ROC value of 0.816, surpassing the baseline GCN model by 11.4%. This highlights the significance of our proposed methodology and Few Shot Learning in advancing Toxic Molecular Classification, with the potential to enhance drug discovery and environmental risk assessment processes.

We present a Task-aware modulation using Representation Learning (TAM-RL) framework that enhances personalized predictions in few-shot settings for heterogeneous systems when individual task characteristics are not known. TAM-RL extracts embeddings representing the actual inherent characteristics of these entities and uses these characteristics to personalize the predictions for each entity/task. Using real-world hydrological and flux tower benchmark data sets, we show that TAM-RL can significantly outperform existing baseline approaches such as MAML and multi-modal MAML (MMAML) while being much faster and simpler to train due to less complexity. Specifically, TAM-RL eliminates the need for sensitive hyper-parameters like inner loop steps and inner loop learning rate, which are crucial for model convergence in MAML, MMAML. We further present an empirical evaluation via synthetic data to explore the impact of heterogeneity amongst the entities on the relative performance of MAML, MMAML, and TAM-RL. We show that TAM-RL significantly improves predictive performance for cases where it is possible to learn distinct representations for different tasks.

For a neural network to learn features from images in order to classify them we need data, lots of data. It is difficult for a model to learn from very few samples per class. MNIST dataset has nearly 60000 training images for numbers 0–9 (10 classes). We will implement One shot learning to build a model which will correctly make predictions given only a single example of each new class. As humans, when we are presented with new object, we quickly pickup patterns, shape and other features.

In traditional Machine Learning domains, we usually take a huge dataset which is specific to a particular task and wish to train a model for regression/classification purposes using this dataset. That's radically far from how humans take advantage of their past experiences to learn very quickly a new task from only a handset of examples. Meta-Learning is essentially learning to learn. Formally, it can be defined as using metadata of an algorithm or a model to understand how automatic learning can become flexible in solving learning problems, hence to improve the performance of existing learning algorithms or to learn (induce) the learning algorithm itself. Each learning algorithm is based on a set of assumptions about the data, which is called its inductive bias.