Learning to Learn: Model Regression Networks for Easy Small Sample Learning

By Yu-Xiong Wang, Martial Hebert. 2016

286 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Representation & Self-Supervised Learning

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Citations in the corpus

Growing a Brain: Fine-Tuning by Increasing Model Capacity (2017, background): “Past techniques have applied such approaches to transfer learning by learning networks that predict models rather than classes [51, 31].”
Rethinking Few-Shot Image Classification: a Good Embedding Is All You Need? (2020, background): “Recently, significant advances [55, 54, 52, 12, 46, 48, 56, 34, 44, 60, 26, 28] have been made to tackle this problem using the ideas of meta-learning or “learning to learn”.”
Meta-Dataset: A Dataset of Datasets for Learning to Learn from Few Examples (2019, background): “The ‘ceiling’ operation ensures that each selected class will have at least one image in the support set.”
The More You Know: Using Knowledge Graphs for Image Classification (2016, background): “eates the input and [37] which learns new categories by ﬁnding common patches between classes. Both evaluate on handwritten characters and it is unclear how well they would scale to realistic images. [36] improves few-shot classiﬁ- cation by training a network on a high-data image dataset and then l”
Probabilistic Model-Agnostic Meta-Learning (2018, background): “A number of works have approached the problem of few-shot learning from a meta-learning perspective [35, 19], including black-box [33, 5, 42] and optimization-based approaches [31, 9].”
Low-Shot Learning from Imaginary Data (2018, background): “An alternative is to directly predict the classifier weights that would be learned from a large dataset using few novel class examples [2] or from a small dataset classifier [37, 38].”
Few-Shot Image Recognition by Predicting Parameters from Activations (2017, background): “Model Regression [31] and SGM [10] are suitable for both large-scale and few-shot image recognition.”
Learning to Reconstruct Shapes from Unseen Classes (2018, background): “More recently, many researchers have begun to study zero- or few-shot recognition with deep networks [Akata et al., 2016, Antol et al., 2014, Hariharan and Girshick, 2017, Wang et al., 2017, Wang and Hebert, 2016].”
The limits and potentials of deep learning for robotics (2018, methodology): “This class-incremental learning would preferably be data-efﬁcient by using one-shot [13], [60], [92], [99], [124] or low-shot [27], [41], [126] learning techniques.”
Boosting Self-Supervised Learning via Knowledge Transfer (2018, methodology): “[34, 37] extend that method to regressing neuron activations, an approach that is more suitable to our case.”
Large-Scale Long-Tailed Recognition in an Open World (2019, methodology): “To directly compare with [54] and [55], we also test on the SUN-LT benchmark they provided.”
The Devil is in the Tails: Fine-grained Classification in the Wild (2017, background): “Our ﬁndings highlight the importance of continued research in transfer and low shot learning [13, 18, 53, 54] and provide baselines for future work to compare against.”
Generalizing from a Few Examples (2019, methodology): “The two most popular applications are character recognition [14, 36, 37, 65, 70, 96, 113, 114, 119, 121, 130, 138, 146] and image classification [37, 70, 96, 106, 119, 121, 127, 130, 132, 138, 142, 143, 149].”
Learning Compositional Representations for Few-Shot Recognition (2018, background): “ng the ﬁrst three categories, shown in red, can a classiﬁer for the fourth category, shown in green, be learned from just a few, or even a single example? This is a problem known as few-shot learning [39,21,18,44,12]. Clearly, it depends on the properties of the representation. Cognitive science ide”
Learning from Small Sample Sets by Combining Unsupervised Meta-Training with CNNs (2016, background): “We test how performance varies with the number of training samples per category as in [16].”
Anti-aliasing Semantic Reconstruction for Few-Shot Semantic Segmentation (2021, background): “While meta-learning [36, 27, 8, 15, 44, 38, 21] contributed important optimization methods and data augmentation [13, 35] aggregated performance, metric learning [32, 30, 11, 5] with prototype models [23, 4, 6, 41, 40, 18] represent the majority of few-shot learning approaches.”