Analyzing Classifiers: Fisher Vectors and Deep Neural Networks (1512.00172v1)

Abstract: Fisher Vector classifiers and Deep Neural Networks (DNNs) are popular and successful algorithms for solving image classification problems. However, both are generally considered `black box' predictors as the non-linear transformations involved have so far prevented transparent and interpretable reasoning. Recently, a principled technique, Layer-wise Relevance Propagation (LRP), has been developed in order to better comprehend the inherent structured reasoning of complex nonlinear classification models such as Bag of Feature models or DNNs. In this paper we (1) extend the LRP framework also for Fisher Vector classifiers and then use it as analysis tool to (2) quantify the importance of context for classification, (3) qualitatively compare DNNs against FV classifiers in terms of important image regions and (4) detect potential flaws and biases in data. All experiments are performed on the PASCAL VOC 2007 data set.

• The paper extends LRP to Fisher Vectors, enabling relevance attribution in complex non-linear mappings.
• Using PASCAL VOC data, it quantitatively reveals that FV classifiers rely more on contextual information while DNNs focus on object-specific features.
• LRP-derived insights guide model evaluation and bias detection, promoting more robust and trustworthy image classification.

Analysis of Fisher Vector Classifiers and Deep Neural Networks Using Layer-Wise Relevance Propagation

The research conducted by Bach et al. explores two predominant image classification techniques—Fisher Vectors (FV) and Deep Neural Networks (DNNs), presenting a novel approach towards understanding their decision-making processes. These classifiers have prominently been treated as "black boxes" due to their complex, non-linear transformations, which hinder detailed interpretability. This paper addresses this interpretative limitation using a technique known as Layer-wise Relevance Propagation (LRP) and assesses its implementation applicability across both FV and DNN models.

The introduction of LRP for FV classifiers represents a notable advancement. Traditionally employed for understanding DNNs, LRP allocates relevance scores to input features, thereby elucidating which features contribute most significantly towards the final prediction. This feature makes LRP invaluable for diagnosing context utilization in image classification and identifying biases within datasets.

The experiments are centered around the PASCAL VOC 2007 dataset, utilizing LRP to dissect and compare the importance of contextual information in classification decisions made by FVs and DNNs. Several key findings merit particular attention:

1. Extension of LRP: The paper successfully extends LRP to Fisher Vectors, highlighting the feasibility of relevance decomposition even when applied to non-linear mappings in FV classifiers. This extension means that FVs, like DNNs, can be made more interpretable.
2. Assessment of Context: Using LRP, the paper defines measures to quantify how each classifier uses context, providing insights that are quantitatively assessed using the "outside-inside relevance ratio" on the PASCAL VOC dataset. The researchers found that FVs tend to utilize contextual information more heavily than DNNs, which focus more on the object rather than its surroundings.
3. Qualitative Comparison of FVs and DNNs: LRP-derived heatmaps reveal that DNNs excel in accurately identifying relevant features, focusing on object shapes and details, whereas FVs frequently rely on correlated contextual background information. This trait of DNNs correlates with higher prediction accuracy on certain classes such as "sheep" and "bird," where FV models often resort to background texture as a class predictor.
4. Relevance in Dataset Bias and Model Evaluation: The paper sheds light on potential biases within the training data that skew predictor reliance towards contextual features. For example, in identifying 'horses', FVs were unreliably influenced by artefactual elements like watermark datasets, suggesting a need for careful dataset curation and model evaluation beyond mere prediction accuracy.

These findings have broad implications for improving model robustness and trustworthiness. For instance, biases can be flagged early during model training, prompting researchers to refine data preprocessing techniques or redefine model architectures. Additionally, utilizing context in moderation could inspire hybrid models harnessing the strengths of both FV and DNN approaches.

Moving forward, this research could inform the development of more sophisticated interpretative tools and further exploration into context-aware architecture designs in AI models. By improving transparency in complex classifiers, researchers can ensure more reliable and ethical deployment of AI systems in practical scenarios, enhancing both safety and performance across diverse application domains. Additionally, understanding the exact architectural features that allow DNNs to outperform FVs offers valuable lessons for enhancing lightweight models (like FV classifiers) in resource-constrained environments.

In conclusion, the integration of LRP with FV classifiers opens new vistas for interpretability in non-linear models, facilitating a deeper understanding of decision pathways and contextual dependencies in AI-driven tasks. This line of inquiry is pivotal for advancing AI transparency and accountability, especially as these technologies continue to permeate critical sectors.