Cardinality-Aware Set Prediction and Top-$k$ Classification
We present a detailed study of cardinality-aware top-$k$ classification, a novel approach that aims to learn an accurate top-$k$ set predictor while maintaining a low cardinality. We introduce a new target loss function tailored to this setting that accounts for both the classification error and the cardinality of the set predicted. To optimize this loss function, we propose two families of surrogate losses: cost-sensitive comp-sum losses and cost-sensitive constrained losses. Minimizing these loss functions leads to new cardinality-aware algorithms that we describe in detail in the case of both top-$k$ and threshold-based classifiers. We establish $H$-consistency bounds for our cardinality-aware surrogate loss functions, thereby providing a strong theoretical foundation for our algorithms. We report the results of extensive experiments on CIFAR-10, CIFAR-100, ImageNet, and SVHN datasets demonstrating the effectiveness and benefits of our cardinality-aware algorithms.
Code (0)
등록된 구현이 없습니다.
Tasks
ClassificationPredictionMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Top-$k$ Classification and Cardinality-Aware Prediction
We present a detailed study of top-$k$ classification, the task of predicting the $k$ most probable classes for an input, extending beyond single-class prediction. We demonstrate that several prevalent surrogate loss fun…
ClassificationMulti-class ClassificationPredictionPredict and Constrain: Modeling Cardinality in Deep Structured Prediction
Many machine learning problems require the prediction of multi-dimensional labels. Such structured prediction models can benefit from modeling dependencies between labels. Recently, several deep learning approaches to st…
BIG-bench Machine LearningDeep LearningGeneral ClassificationMulti-Label Classification+3When Can Test-Time Adaptation Help Zero-Shot CT Vision-Language Models?
3D CT vision-language models (VLMs) classify abnormalities from text prompts in a zero-shot manner, enabling cross-institution deployment where labels are scarce and clinical tasks shift faster than supervised models can…
Test-time AdaptationUsing Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks
High-cardinality categorical features are a major challenge for machine learning methods in general and for deep learning in particular. Existing solutions such as one-hot encoding and entity embeddings can be hard to sc…
Entity EmbeddingsDeepSetNet: Predicting Sets with Deep Neural Networks
This paper addresses the task of set prediction using deep learning. This is important because the output of many computer vision tasks, including image tagging and object detection, are naturally expressed as sets of en…
image-classificationImage ClassificationObject Countingobject-detection+2