This paper introduces a probabilistic framework for k-shot image classification. The goal is to generalise from an initial large-scale classification task to a separate task comprising new  classes and small numbers of examples. The new approach not only leverages the feature-based representation learned by a neural network from the initial task (representational transfer), but also information about the classes (concept transfer). The concept information is encapsulated in a probabilistic model for the final layer weights of the neural network which acts as a prior for probabilistic k-shot learning. We show that even a simple probabilistic model achieves state-of-the-art on a standard k-shot learning dataset by a large margin. Moreover, it is able to accurately model uncertainty, leading to well calibrated classifiers, and is easily extensible and flexible, unlike many recent approaches to k-shot learning. A child encountering images of helicopters for the first time is able to generalize to instances with radically different appearance from only a handful of labelled examples. This remarkable feat is supported in part by a high-level feature-representation of images acquired from past experience. However, it is likely that information about previously learned concepts, such as aeroplanes and vehicles, is also leveraged (e.g. that sets of features like tails and rotors or objects like pilots/drivers are likely to appear in new images). The goal of this paper is to build machine systems for performing k-shot learning, which leverage both existing feature representations of the inputs and existing class information that have both been honed by learning from large amounts of labelled data.K-shot learning has enjoyed a recent resurgence in the academic community BID6 BID5 BID15 BID12 . Current stateof-the-art methods use complex deep learning architectures and claim that learning good features for k-shot learning entails training for k-shot specifically via episodic training that simulates many k-shot tasks. In contrast, this paper proposes a general framework based upon the combination of a deep feature extractor, trained on batch classification, and traditional probabilistic modelling. It subsumes two existing approaches in this vein BID1 , and is motivated by similar ideas from multi-task learning BID0 . The intuition is that deep learning will learn powerful feature representations, whereas probabilistic inference will transfer top-down conceptual information from old classes. Representational learning is driven by the large number of training examples from the original classes making it amenable to standard deep learning. In contrast, the transfer of conceptual information to the new classes relies on a relatively small number of existing classes and k-shot data points, which means probabilistic inference is appropriate.While generalisation accuracy is often the key objective when training a classifier, calibration is also a fundamental concern in many applications such as decision making for autonomous driving and medicine. Here, calibration refers to the agreement between a classifier's uncertainty and the frequency of its mistakes, which has recently received increased attention. For example, show that the calibration of deep architectures deteriorates as depth and complexity increase. Calibration is closely related to catastrophic forgetting in continual learning. However, to our knowledge, uncertainty has so far been over-looked by the k-shot community even though it is high in this setting.Our basic setup mimics that of the motivating example above: a standard deep convolutional neural network (CNN) is trained on a large labelled training set. This learns a rich representation of images at the top hidden layer of the CNN. Accumulated knowledge about classes is embodied in the top layer softmax weights of the network. This information is extracted by training a probabilistic model on these weights. K-shot learning can then 1) use the representation of images provided by the CNN as input to a new softmax function, 2) learn the new softmax weights by combining prior information about their likely form derived from the original dataset with the k-shot likelihood.The main contributions of our paper are: 1) We propose a probabilistic framework for k-shot learning. It combines deep convolutional features with a probabilistic model that treats the top-level weights of a neural network as data, which can be used to regularize the weights at k-shot time in a principled Bayesian fashion. We show that the framework recovers L 2 -regularised logistic regression, with an automatically determined setting of the regularisation parameter, as a special case.2) We show that our approach achieves state-of-the-art results on the miniImageNet dataset by a wide margin of roughly 6% for 1-and 5-shot learning. We further show that architectures with better batch classification accuracy also provide features which generalize better at k-shot time. This finding is contrary to the current belief that episodic training is necessary for good performance and puts the success of recent complex deep learning approaches to k-shot learning into context.3) We show on miniImageNet and CIFAR-100 that our framework achieves a good trade-off between classification accuracy and calibration, and it strikes a good balance between learning new classes and forgetting the old ones. We present a probabilistic framework for k-shot learning that exploits the powerful features and class information learned by a neural network on a large training dataset. Probabilistic models are then used to transfer information in the network weights to new classes. Experiments on miniImageNet using a simple Gaussian model within our framework achieve state-of-the-art for 1-shot and 5-shot learning by a wide margin, and at the same time return well calibrated predictions. This finding is contrary to the current belief that episodic training is necessary to learn good k-shot features and puts the success of recent complex deep learning approaches to k-shot learning into context. The new approach is flexible and extensible, being applicable to general discriminative models and kshot learning paradigms. For example, preliminary results on online k-shot learning indicate that the probabilistic framework mitigates catastrophic forgetting by automatically balancing performance on the new and old classes.The Gaussian model is closely related to regularised logistic regression, but provides a principled and fully automatic way to regularise. This is particularly important in k-shot learning, as it is a low-data regime, in which cross-validation performs poorly and where it is important to train on all available data, rather than using validation sets.Appendix to "Discriminative k-shot learning using probabilistic models"A DETAILS ON THE DERIVATION AND APPROXIMATIONS FROM SEC. 2.1As stated in the main text, the probabilistic k-shot learning approach comprises four phases mirroring the dataflow:Phase 1: Representational learning. The large dataset D is used to train the CNN Φ ϕ using standard deep learning optimisation approaches. This involves learning both the parameters ϕ of the feature extractor up to the last hidden layer, as well as the softmax weights W. The network parameters ϕ are fixed from this point on and shared across phases. This is a standard setup for multitask learning and in the present case it ensures that the features derived from the representational learning can be leveraged for k-shot learning.Phase 2: Concept learning. The softmax weights W are effectively used as data for concept learning by training a probabilistic model that detects structure in these weights which can be transferred for k-shot learning. This approach will be justified in the next section. For the moment, we consider a general class of probabilistic models in which the two sets of weights are generated from shared hyperparameters θ, so that p( W, W, θ) = p(θ)p( W|θ)p(W|θ) (see FIG0 ).Phases 3 and 4: k-shot learning and testing. Probabilistic k-shot learning leverages the learned representation Φ ϕ from phase 1 and the probabilistic model p( W, W, θ) from phase 2 to build a (posterior) predictive model for unseen new examples using examples from the small dataset D.
This paper introduces a probabilistic framework for k-shot image classification that achieves state-of-the-art results
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