Dense Classification and Implanting for Few-Shot Learning

By Yann Lifchitz, Yannis Avrithis, Sylvaine Picard, Andrei Bursuc. 2019

206 citations recorded by the source corpus.

Few-shot learning for deep neural networks is a highly challenging and key problem in many computer vision tasks. In this context, we are targeting knowledge transfer from a set with abundant data to other sets with few available examples. We propose two simple and effective solutions: (i) dense classification over feature maps, which for the first time studies local activations in the domain of few-shot learning, and (ii) implanting, that is, attaching new neurons to a previously trained network to learn new, task-specific features. Implanting enables training of multiple layers in the few-shot regime, departing from most related methods derived from metric learning that train only the final layer. Both contributions show consistent gains when used individually or jointly and we report state of the art performance on few-shot classification on miniImageNet.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Meta & Continual Learning

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Citations in the corpus

CrossTransformers: spatially-aware few-shot transfer (2020, background): “[49] learns spatially dense classifiers for few-shot classification, but pools the spatial dimensions of the prototypes, and hence does not have a notion of correspondence.”
Learning Adaptive Classifiers Synthesis for Generalized Few-Shot Learning (2019, background): “FSL has achieved promising results in various domains such as visual recognition [13, 38, 57], domain adaptation [14, 24], neural machine translation [19], data compression [64], and density estimation [48].”
Boosting Few-Shot Visual Learning With Self-Supervision (2019, neutral): “, training images, of a few-shot task [26, 33, 53, 55, 57, 59]; methods learning to map a test example to a class label by accessing memory modules that store training examples for that task [11, 25, 35, 38, 50]; approaches that learn how to generate the weights of a classifier [12, 16, 43, 44, 13] ”
Few-Shot Object Detection With Attention-RPN and Multi-Relation Detector (2019, background): “A number of works [41, 42] exploit local descriptors to reap additional knowledge from limited data.”
Cross-Domain Few-Shot Classification via Learned Feature-Wise Transformation (2020, methodology): “Third, metric-based methods (Koch et al., 2015; Vinyals et al., 2016; Snell et al., 2017; Oreshkin et al., 2018; Sung et al., 2018; Lifchitz et al., 2019) classify the query images by computing the similarity between the query image and few labeled images of novel categories.”
A Comprehensive Survey of Few-shot Learning: Evolution, Applications, Challenges, and Opportunities (2022, methodology): “[76] replaced the standard activation function with cosine similarity, and Nakamura et al.”
Aligning Visual Prototypes with BERT Embeddings for Few-Shot Learning (2021, background): “Most few-shot learning methods can be divided into metric-based [17, 37, 43, 55] and meta-learning based [7, 25, 34] methods, although some other directions have also been explored, such as hallucination based [11, 51, 58] and parameter-generation based [9, 26] methods.”
DeepEMD: Few-Shot Image Classification With Differentiable Earth Mover’s Distance and Structured Classifiers (2020, background): “Optimization-based methods [2, 5, 9–11, 18, 26, 31–33, 36, 38, 40–42, 44, 46, 47, 54, 55, 58, 64, 79] target at effectively adapting model parameters to new tasks in the low-shot regime.”
Boosting Few-Shot Learning With Adaptive Margin Loss (2020, unknown): “DC [17] ResNet12 Metric 62.”
DeepEMD: Differentiable Earth Mover's Distance for Few-Shot Learning (2020, background): “[38] directly make predictions with each local feature and fuse their results.”