Source-linked AI summary
Improving Interpretability of Deep Neural Networks with Semantic Information
Yinpeng Dong, Hang Su, Jun Zhu, Bo Zhang
TL;DR
DNNs’ opaque nature makes it difficult to understand their behavior and decisions, limiting users’ ability to assess and correct them. The paper uses semantic topics from video descriptions to guide learning, interprets neuron features, and enables human revision; it reports improved video-captioning performance and interpretable features transferable to action recognition.
Problem
DNNs’ black-box nature makes their decisions difficult to understand, while prior interpretation methods often focus on simpler architectures and operate only after training.
Source: §1 · p. 2Method
For video captioning, the paper derives semantic topics from human descriptions, integrates them into training with an interpretive loss, and uses prediction difference maximization to interpret neuron features.
Source: §1 · p. 2Results
The method achieves better video-captioning performance than various competitors, yields more interpretable features than opaque models, and transfers those features to video action recognition.
Source: §5 · p. 8Takeaways & Limitations
The human-in-the-loop procedure lets users use interpretable features to revise false predictions and refine the network.
Source: §5 · p. 8
Abstract
from arXiv · showhide
Interpretability of deep neural networks (DNNs) is essential since it enables users to understand the overall strengths and weaknesses of the models, conveys an understanding of how the models will behave in the future, and how to diagnose and correct potential problems. However, it is challenging to reason about what a DNN actually does due to its opaque or black-box nature. To address this issue, we propose a novel technique to improve the interpretability of DNNs by leveraging the rich semantic information embedded in human descriptions. By concentrating on the video captioning task, we first extract a set of semantically meaningful topics from the human descriptions that cover a wide range of visual concepts, and integrate them into the model with an interpretive loss. We then propose a prediction difference maximization algorithm to interpret the learned features of each neuron. Experimental results demonstrate its effectiveness in video captioning using the interpretable features, which can also be transferred to video action recognition. By clearly understanding the learned features, users can easily revise false predictions via a human-in-the-loop procedure.
1. Introduction
DNNs can be difficult to understand, trust, and correct because their internal features and decision rationales are opaque. The paper addresses limits in prior interpretation methods by integrating semantic information from video descriptions into training and supporting human revision of false predictions.
1. Introduction
- DNNs’ black-box nature makes it difficult to understand their learned features and decision rationales, anticipate behavior, and correct potential errors.This can leave model development reliant on trial and error and is especially concerning in settings where users need to assess system decisions.Source: §1 · pp. 1–2
- Prior interpretation methods often analyze simpler architectures after training, limiting their ability to produce interpretable models that users can interact with.The separation of learning and interpretation makes human interaction, such as correcting errors, difficult.Source: §1 · p. 2
- The paper uses semantic topics extracted from video descriptions to guide learning through an interpretive loss.The topics represent visual concepts including objects, actions, relationships, and object mood or status.Source: §1 · p. 2
- A prediction difference maximization algorithm interprets learned features, while a human-in-the-loop procedure lets users revise false predictions.The authors report that experiments on real-world datasets demonstrate the method’s effectiveness.Source: §1 · p. 2
2. Methodology
The methodology uses topics extracted from human descriptions to guide video-captioning features toward interpretability, then maps learned neurons to topics with prediction difference maximization. Its attentive encoder-decoder combines CNN and bidirectional LSTM video features with an LSTM caption decoder.
2.1. Overview
- Description-derived latent topics are integrated through an interpretive loss alongside caption likelihood, guiding learned features toward semantic interpretability.Source: §2.1 · p. 3
2.2. Attentive Encoder-Decoder Framework
- A CNN plus bidirectional LSTM encodes spatial and temporal information, while attention lets an LSTM decoder select frames as it generates captions.Source: §2.2 · pp. 3–4
2.3. Interpretive Loss
- LDA topics summarize corpus semantics, and a binary video-topic vector supervises a feature-to-topic predictor through an L2 interpretive loss.Source: §2.3 · pp. 4–5
2.4. Prediction Difference Maximization
- Prediction difference maximization assigns neurons to topics by identifying which neuron contributes most to predicting a topic present in a video; although neurons may prefer multiple topics, one is selected for simplicity.Source: §2.4 · p. 5
3. Experimental Results
LSTM-I’s interpretable features accompany state-of-the-art video-captioning performance and outperform LSTM-B in video action recognition. Visualizations show topic-linked neuron activations, while transfer results compare caption-trained features with task-specific and randomly initialized alternatives.
3.1. Experimental Settings
- Experiments use YouTubeClips (1,970 clips; 1,200/100/670 train/validation/test) and compare matched encoder-decoder LSTM-I and LSTM-B, with interpretive loss used only by LSTM-I.The frame-wise GoogLeNet features are fixed during training.Source: §3.1 · p. 5
3.2. Feature Visualization
- LSTM-I representations show peaky activations at specific neurons for sampled topics, unlike LSTM-B, supporting topic-neuron correspondence and easier feature interpretation.Figure 3 shows average activations for videos sharing the topics dog, girl, walk, and dance.Source: §3.2 · pp. 5–6, Figure 3 · p. 6
3.3. Performance Comparison
- LSTM-I reaches state-of-the-art captioning performance and higher BLEU than other methods; against LSTM-B it significantly improves BLEU and improves METEOR.Table 2 compares description-generation BLEU and METEOR scores on YouTubeClips.Source: Table 2 · p. 6, §3.3 · p. 6
3.2. Feature Visualization
- Relevant-topic neurons activate strongly through videos, although they need not activate in every frame because topic mapping uses average video features.Figure 4 plots activations over time for neurons associated with relevant and irrelevant topics.Source: §3.2 · p. 6, Figure 4 · p. 7
3.4. Video Action Recognition
- On UCF11 action recognition, LSTM-I achieves higher accuracy than LSTM-B and LSTM-R, while the caption-trained encoders remain fixed for LSTM-I and LSTM-B.LSTM-R uses the same architecture with randomly initialized parameters that are optimized for action recognition.Source: §3.4 · p. 7
4. Human-in-the-loop Learning
The procedure lets users correct inaccurate captions by supplying missing topics, then propagates those corrections to unseen video segments. In 17 of 20 test videos, refining on the first half produced more accurate captions for the second half.
4. Human-in-the-loop Learning
- 17 of 20 videos received more accurate captions on their unseen second halves after refinement using the first halves.The figure illustrates four cases in which users supplied missing topics before refinement.Source: Figure 5 · p. 8, §4 · p. 8
- For an inaccurate caption, users supply a missing topic, and the procedure boosts activations of neurons associated with that topic.It adds those neurons’ average activations from training videos containing the topic to the original features.Source: §4 · p. 8
- Correction propagation fine-tunes the encoder to generate the enhanced features while limiting parameter variation to reduce overfitting.The objective balances approximating the target features against keeping the refined model close to the original.Source: §4 · p. 8
- BLEU 0.449 and METEOR 0.298 were slightly better than the corresponding Table 2 scores after refining the 20 videos.The authors report that refinement did not affect overall captioning ability while improving predictions for the selected videos.Source: §4 · p. 8
5. Conclusions
The paper presents a video-captioning approach that uses human descriptions to make DNN features interpretable. Its experiments report improved captioning performance, transferable interpretable features, and human-in-the-loop revision of false predictions.
5. Conclusions
- Semantically meaningful topics extracted from human descriptions are integrated into training through an interpretive loss.The topics are used to improve the interpretability of learned features while aiming to achieve high performance.Source: §5 · p. 8
- Prediction difference maximization represents each neuron’s learned features with a topic, and human users can revise false predictions to refine the network.The paper demonstrates this human-in-the-loop training procedure.Source: §5 · p. 8
- The method outperforms various competitors in video captioning, learns more interpretable features transferable to action recognition, and demonstrates human-in-the-loop refinement.The authors also report examples showing the refinement procedure’s effectiveness.Source: §5 · p. 8