Meta-Learning with Latent Embedding Optimization (1807.05960v3)

Abstract: Gradient-based meta-learning techniques are both widely applicable and proficient at solving challenging few-shot learning and fast adaptation problems. However, they have practical difficulties when operating on high-dimensional parameter spaces in extreme low-data regimes. We show that it is possible to bypass these limitations by learning a data-dependent latent generative representation of model parameters, and performing gradient-based meta-learning in this low-dimensional latent space. The resulting approach, latent embedding optimization (LEO), decouples the gradient-based adaptation procedure from the underlying high-dimensional space of model parameters. Our evaluation shows that LEO can achieve state-of-the-art performance on the competitive miniImageNet and tieredImageNet few-shot classification tasks. Further analysis indicates LEO is able to capture uncertainty in the data, and can perform adaptation more effectively by optimizing in latent space.

Meta-Learning with Latent Embedding Optimization

The paper "Meta-Learning with Latent Embedding Optimization" presents a novel approach to addressing the challenges inherent in few-shot learning tasks. As a technique, gradient-based meta-learning has shown efficacy in few-shot learning scenarios, but struggles with high-dimensional parameter spaces and low-data regimes. This work introduces Latent Embedding Optimization (LEO), an approach to decouple the gradient-based adaptation procedure from the high-dimensional model parameter space by operating in a learned low-dimensional latent space.

Background and Problem Definition

Few-shot learning aims to enable models to quickly adapt to new tasks with minimal training examples. Traditional deep learning approaches are data-intensive and inefficient in this regime. Meta-learning, particularly optimization-based meta-learning such as Model-Agnostic Meta-Learning (MAML), seeks to find a universal set of model parameters that can be effectively adapted to various tasks with few gradient descent steps. However, performing such adaptations directly in the high-dimensional parameter space poses significant challenges due to overfitting and inefficient generalization.

Latent Embedding Optimization (LEO)

LEO proposes to address these issues by learning a data-dependent latent generative representation of model parameters. This approach leverages a lower-dimensional latent space for gradient-based meta-learning, which achieves two primary advantages:

1. Initial parameters for any task are conditioned on the training data, facilitating task-specific adaptations.
2. Gradient-based optimization operates in the latent space, making the adaptation process more effective by expressing ambiguities in the few-shot data regime.

Model Architecture

The paper provides a detailed description of the model architecture and the LEO training procedure:

1. Encoding: Input data samples from a task instance are processed through an encoder to produce a low-dimensional latent code. The encoder includes a relational network to consider pairwise relationships among data points, which allows the encoded latent code to capture essential contextual information.
2. Decoding: The latent code is then decoded to instantiate model parameters. The decoder maps from the latent space to high-dimensional model parameters, which serve as initializations for the model.
3. Adaptation: The adaptation occurs over several steps in the latent space using gradient descent, modifying the latent code, followed by decoding to obtain the adjusted model parameters tuned for the specific task. An optional fine-tuning step in the parameter space further refines the model.
4. Meta-Training: The entire process is geared towards minimizing a meta-learning objective that combines the validation loss for a task instance and regularization terms to encourage disentangled and expressive latent representations.

Empirical Evaluation

The authors validate their approach on few-shot regression and classification tasks. For regression, LEO effectively models a multimodal task distribution, capturing uncertainties and generating diverse yet accurate parameters for noisy sine and linear functions. On the classification front, LEO achieves state-of-the-art performance on the challenging few-shot learning benchmarks of the Mini-ImageNet and Tiered-ImageNet datasets. The paper further includes an ablation paper that underscores the importance of both the conditional parameter generation and the latent space optimization stages.

Theoretical and Practical Implications

The LEO method has several implications:

• Theoretical: It demonstrates the efficacy of performing meta-learning in a learned latent space, showing that it not only aids in handling high-dimensional parameter spaces but also effectively captures the uncertainty in the few-shot regime.
• Practical: By pre-training the feature extractor and utilizing latent space optimization, the LEO framework can efficiently learn and adapt models from limited data, promising more effective implementations in practical machine learning systems.

Future Directions

Future research could extend LEO to other domains such as reinforcement learning or sequence modeling, where data efficiency and rapid adaptation are crucial. Additionally, efforts could focus on co-training the feature extractor within the meta-learning framework to potentially improve representation quality and generalization further.

In conclusion, "Meta-Learning with Latent Embedding Optimization" introduces a robust technique that advances the state-of-the-art in few-shot learning through innovative latent space modeling and adaptation strategies. This approach not only enhances generalization but also provides a scalable solution to the problem of meta-learning within complex and high-dimensional parameter spaces.