Delta-encoder: an effective sample synthesis method for few-shot object recognition (1806.04734v3)

Abstract: Learning to classify new categories based on just one or a few examples is a long-standing challenge in modern computer vision. In this work, we proposes a simple yet effective method for few-shot (and one-shot) object recognition. Our approach is based on a modified auto-encoder, denoted Delta-encoder, that learns to synthesize new samples for an unseen category just by seeing few examples from it. The synthesized samples are then used to train a classifier. The proposed approach learns to both extract transferable intra-class deformations, or "deltas", between same-class pairs of training examples, and to apply those deltas to the few provided examples of a novel class (unseen during training) in order to efficiently synthesize samples from that new class. The proposed method improves over the state-of-the-art in one-shot object-recognition and compares favorably in the few-shot case. Upon acceptance code will be made available.

• The paper introduces the Δ-encoder, which learns transferable intra-class deformations to generate synthetic training samples from limited examples.
• It leverages a modified auto-encoder framework with non-linear delta encoding to improve few-shot object recognition performance.
• Empirical evaluations on datasets like miniImageNet and CIFAR-100 demonstrate the approach’s ability to address data scarcity effectively.

An Assessment of the $\Delta$-encoder for Few-shot Object Recognition

This paper addresses the challenge of few-shot object recognition, presenting a novel approach named the $\Delta$-encoder. Few-shot learning is concerned with training models to accurately recognize object categories based on only a few training examples. This remains a significant issue within computer vision, especially when contrasted against human capability to efficiently categorize objects after minimal exposure. A common technique in machine learning relies on accessing large labeled datasets which are often impractical or expensive to acquire across all domains.

Overview of the $\Delta$-encoder Approach

The $\Delta$-encoder represents a modified auto-encoder framework, designed to synthesize samples for unseen categories based on minimal exemplars. The core innovation within this approach is the learning of "deltas" – transferable intra-class deformations extracted from same-class pairs during the training phase. During testing, these deltas are applied to a sparse set of examples from novel categories, facilitating the generation of synthetic samples, subsequently used to train classifiers.

This method improves state-of-the-art performance in one-shot object recognition and offers comparative results in few-shot scenarios. Specifically, the empirical validation utilizing standard datasets (miniImageNet, CIFAR-100, Caltech-256, and CUB among others) demonstrates competitive or superior performance relative to existing few-shot learning methods. On average, the $\Delta$-encoder approach yields significant improvements over baseline and advanced methods, such as MAML, Prototypical Networks, and Dual TriNet, particularly when leveraging pre-trained feature extractors.

Key Contributions and Experimental Analysis

The distinctive aspect of the $\Delta$-encoder compared to other generative methods is its focus on encoding non-linear transformations as deltas within a latent space. This is in contrast to strategies that apply direct transformations such as linear offsets. The auto-encoder structure, comprised of an encoder that derives a low-dimensional representation of these deltas, and a decoder that synthesizes new samples using these deltas, demonstrates an ability to extrapolate beyond the provided examples, populating the feature space with additional synthesized data points.

Testing illustrates that this method can effectively capitalize on limited data, with the synthesized examples significantly improving model performance in scenarios provided with only minimal examples from unseen classes. The paper also explores a comparative analysis of different design choices, reinforcing the necessity of non-linear encoding mechanisms for successful sample synthesis.

The performed ablation studies highlight the importance of each architectural component of the $\Delta$-encoder, validating the design decisions through systematic experimentation. The paper also examines the relationship of the synthesized samples to real example embeddings within the feature space, offering evidence of non-trivial sample synthesis.

Theoretical and Practical Implications

The implications of this work are notable both theoretically and practically. Theoretically, the $\Delta$-encoder contributes to the discourse on leveraging learned feature space transformations to combat the data sparsity challenge in machine learning. It accentuates the potential of using intra-class variance as a synthetic data generation tool, bridging gaps in categorical representation without reliance on rich datasets. Practically, the approach has utility in domains where data collection might be constrained, offering an opportunity to expand training sets sans labeled data.

Future Directions

Looking forward, several future directions surface. Exploration into an end-to-end learning paradigm, incorporating the feature extraction with the $\Delta$-encoder framework, may provide further performance boosts. Additionally, integrating this method with semi-supervised or active learning protocols could offer practical benefits in application areas characterized by limited data availability. Finally, expanding the understanding of the $\Delta$-encoder in various architectures and deployment in diverse domains represents an intriguing avenue for further research.

Overall, the $\Delta$-encoder presents a promising technique for few-shot learning, highlighting the utility of sample synthesis through learned intra-class deformations.