Model-Agnostic Few-Shot Open-Set Recognition

We tackle the Few-Shot Open-Set Recognition (FSOSR) problem, <span>*i*.*e*., </span>classifying instances among a set of classes for which we only have few labeled samples, while simultaneously detecting instances that do not belong to any known class. Departing from existing literature, we focus on developing model-agnostic inference methods that can be plugged into any existing model, regardless of its architecture or its training procedure. Through evaluating the embedding's quality of a variety of models, we quantify the intrinsic difficulty of model-agnostic FSOSR. Furthermore, a fair empirical evaluation suggests that the naive combination of a <span>$k$-<span style="font-variant:small-caps;">NN</span></span>detector and a prototypical classifier ranks before specialized or complex methods in the inductive setting of FSOSR. These observations motivated us to resort to *transduction*, as a popular and practical relaxation of standard few-shot learning problems. We introduce an Open Set Transductive Information Maximization method (<span><span style="font-variant:small-caps;">OSTIM</span></span>), which hallucinates an outlier prototype while maximizing the mutual information between extracted features and assignments. Through extensive experiments spanning 5 datasets, we show that <span><span style="font-variant:small-caps;">OSTIM</span></span>surpasses both inductive and existing transductive methods in detecting open-set instances while competing with the strongest transductive methods in classifying closed-set instances. We further show that <span><span style="font-variant:small-caps;">OSTIM</span></span>'s model agnosticity allows it to successfully leverage the strong expressive abilities of the latest architectures and training strategies without any hyperparameter modification, a promising sign that architectural advances to come will continue to positively impact <span><span style="font-variant:small-caps;">OSTIM</span></span>'s performances. [^1].

Discussion and Limitations {#sec:discussions}
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In this study, we advocate for Transduction as a promising avenue to address the difficult FSOSR problem. Through the proposed implicit prototype idea, we show that the InfoMax method TIM can be successfully *opened*. We further insist that the proposed technique does not necessitate any particular training process or model-specific parameter optimization, and can therefore be plugged effortlessly into the most expressive feature extractors. We hope that the promising results we obtained in this setting, using the latest advances in representation learning, will encourage the community to go beyond small residual networks and leverage these advances in our methods more often than we do today. Taking a step back, the opening technique we propose is in fact broadly applicable, and other transductive classification methods, <span>*e*.*g*., </span>LaplacianShot or PT-MAP could be similarly *opened*. In that regard, attempts at *opening* other existing methods would broaden the scope of the current study. As a second limitation, we note that Transduction does not come for free. In particular, using unlabelled data makes transductive methods inherently sensitive to the statistical properties of the query set, <span>*e*.*g*., </span>class balance or size. Such properties may greatly vary from one practical situation to another. Consequently, an in-depth study of the impact of such properties on the performances of <span><span style="font-variant:small-caps;">OSTIM</span></span>would surely help better identify the situations in which Transduction should be preferred over Induction, and vice-versa.

[^1]: Code is available at <https://github.com/ebennequin/few-shot-open-set>

[^2]: Equal contribution. Corresponding authors: {michelle99@example.com, michelle99@example.com}

{'source': 'arXiv_out/2206'}