Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles (1606.07839v3)

Abstract: Many practical perception systems exist within larger processes that include interactions with users or additional components capable of evaluating the quality of predicted solutions. In these contexts, it is beneficial to provide these oracle mechanisms with multiple highly likely hypotheses rather than a single prediction. In this work, we pose the task of producing multiple outputs as a learning problem over an ensemble of deep networks -- introducing a novel stochastic gradient descent based approach to minimize the loss with respect to an oracle. Our method is simple to implement, agnostic to both architecture and loss function, and parameter-free. Our approach achieves lower oracle error compared to existing methods on a wide range of tasks and deep architectures. We also show qualitatively that the diverse solutions produced often provide interpretable representations of task ambiguity.

• The paper introduces Stochastic Multiple Choice Learning (sMCL), a novel, architecture-agnostic method to train diverse deep ensembles by directly minimizing oracle loss using stochastic gradient descent.
• Experimental results demonstrate sMCL's effectiveness across image classification, semantic segmentation, and image captioning tasks, showing improved oracle accuracy, IoU, and caption diversity compared to baselines.
• sMCL promotes emergent specialization among ensemble members, offering a promising direction for efficient, robust multi-output solutions in AI and advancing the ensemble learning paradigm.

Stochastic Multiple Choice Learning for Training Diverse Deep Ensembles: An Overview

The paper presents a novel approach named Stochastic Multiple Choice Learning (sMCL) aimed at enhancing ensemble learning through diversification in deep networks. It addresses the task of generating multiple plausible outputs rather than a single solution for perception challenges in various AI domains such as Computer Vision and Natural Language Processing. The approach is designed to be simple, architecture-agnostic, parameter-free, and involves stochastic gradient descent (SGD) optimization to directly minimize oracle loss.

Core Idea and Methodology

In ensemble learning, the typical goal is to reduce error by leveraging diversity among models. The paper introduces the concept of ensuring diversity, not through traditional resampling or decorrelating data techniques, but by directly minimizing oracle loss. In Multiple Choice Learning (MCL), the goal is to train a set of models such that an oracle, which can select the best prediction, achieves minimal error. The authors propose an adaptation of this concept using SGD—a method called Stochastic Multiple Choice Learning (sMCL).

sMCL operates by interleaving data assignment and model training in mini-batches, exploiting the network's differentiable nature. Instead of training models sequentially or requiring multiple iterations to update predictions, sMCL updates the model parameters only for the best-performing model in each iteration, thereby training all ensemble members simultaneously and reducing computational overhead.

Experimental Evaluations

The paper provides empirical evidence supporting the efficacy of sMCL, across three main tasks: image classification, semantic segmentation, and image captioning, using diverse architectures such as CNNs, FCNs, and LSTMs respectively.

1. Image Classification: On the CIFAR10 dataset, sMCL achieved higher oracle accuracy than existing methods and classical ensembles. Notably, sMCL proved faster than traditional MCL approaches without compromising performance.
2. Semantic Segmentation: Experiments on the Pascal VOC dataset demonstrated sMCL's superiority in mean Intersection over Union (IoU) over other ensemble methods. The analysis highlighted its capacity to produce diverse segmentations that capture task ambiguities effectively.
3. Image Captioning: Through experiments on the MSCOCO dataset, sMCL not only increased oracle CIDEr-D scores but also enhanced the diversity of generated n-grams, achieving more informative and varied captions compared to baseline techniques.

Implications and Future Directions

The findings underscore the potential of sMCL to foster emergent specialization among ensemble models, leading to improved task performance and diversity in predictions. While sMCL lays the groundwork for efficient and robust ensemble learning in deep networks without architectural constraints, future work could explore its application across more complex tasks and larger datasets.

Moreover, as AI applications require more robust and comprehensive multi-output solutions, sMCL offers a promising direction. It could drive advancements in uncertainty quantification and decision-making processes where multiple plausible outcomes are beneficial, particularly in real-time or resource-constrained environments.

The paper offers a compelling contribution to the ensemble learning paradigm, presenting avenues for further theoretical exploration and practical refinement in the rapidly evolving landscape of artificial intelligence.