Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks

מושגי ליבה

Proposing a novel debiasing framework, Debiasing Global Workspace, using attention-based information bottlenecks to learn compositional representations of attributes without bias types.

תקציר

Authors from Arizona State University propose a debiasing framework.
Focus on learning intrinsic and biased attributes for improved performance.
Attention-based information bottlenecks aid in attribute-centric representation learning.
Evaluation on biased datasets showcases efficacy in capturing attribute representation.

סטטיסטיקה

Biased attributes can lead to neural networks learning improper shortcuts for classifications.
Debiasing approaches aim to ensure correct predictions from biased datasets.
Training models with generalized cross-entropy loss improves robustness on easy-to-learn features.

ציטוטים

"Models trained on biased datasets face the issue of strongly favoring bias-aligned samples."
"Debiasing Global Workspace introduces attention-based information bottlenecks for learning compositional representations of attributes."

תובנות מפתח מזוקקות מ:

Learning Decomposable and Debiased Representations via Attribute-Centric Information Bottlenecks

by Jinyung Hong... ב- arxiv.org 03-22-2024

https://arxiv.org/pdf/2403.14140.pdf

שאלות מעמיקות

How can the proposed framework be applied to real-world data effectively

The proposed framework can be effectively applied to real-world data by following a structured approach. Firstly, the model needs to be trained on diverse and representative datasets that reflect the complexities of real-world scenarios. This ensures that the model learns robust representations of intrinsic and biased attributes. Additionally, incorporating attention-based mechanisms like Attribute-Slot-Attention (ASA) and Cross-Attention (CA) modules allows for interpretable explanations about how the model pays attention to different attributes in the data.
Furthermore, it is essential to validate the performance of the framework on various benchmarks and real-world datasets to ensure its efficacy in debiasing and attribute-centric representation learning tasks. Conducting thorough evaluations with quantitative metrics such as accuracy, Expected Calibration Error (ECE), Negative Log Likelihood (NLL), t-SNE clustering analysis, and visualization techniques helps in assessing the model's generalizability, reliability, interpretability, and ability to distinguish between intrinsic and bias-related features accurately.
Overall, by following a systematic training process on diverse datasets, leveraging attention mechanisms for interpretability, validating performance on relevant benchmarks, and conducting comprehensive evaluations using appropriate metrics, the proposed framework can be successfully applied to real-world data effectively.

What are the potential limitations of not defining specific bias types in the debiasing process

One potential limitation of not defining specific bias types in the debiasing process is related to adaptability across different domains or datasets. When specific bias types are not predefined or identified beforehand, there might be challenges in understanding which attributes are contributing towards biases within a particular dataset. This could lead to suboptimal debiasing results or difficulties in interpreting how biases are being addressed by the model.
Moreover, without defining specific bias types upfront, there may be instances where certain biases go unnoticed or unaddressed during training. This lack of specificity could result in incomplete mitigation of biases present in the data or unintended reinforcement of certain biased patterns due to inadequate identification strategies.
Therefore, while not defining specific bias types offers flexibility and avoids assumptions about underlying biases that may vary across datasets or contexts; it also poses challenges in precisely targeting and mitigating those biases effectively without clear guidance on what aspects need correction.

How can shape-centric representation learning impact other areas beyond image classification

Shape-centric representation learning has implications beyond image classification into various other areas such as natural language processing (NLP), speech recognition,
and reinforcement learning among others:

Natural Language Processing: In NLP tasks like sentiment analysis or text classification,
shape-centric representations can help capture structural information within textual data.

Speech Recognition: For speech recognition systems,
shape-centric learning can aid in identifying phonetic patterns
crucial for accurate transcription.

Reinforcement Learning: In reinforcement learning environments,
understanding shape-based features can enhance decision-making processes
based on visual cues rather than texture-specific details.

Healthcare Imaging: Shape-centric representations can improve medical imaging analyses
by focusing on anatomical structures rather than surface textures

5 .Autonomous Vehicles: In autonomous driving systems,
shape-focused models enable better object detection
regardless of variations caused by lighting conditions
By applying shape-centric representation learning methodologies across these domains,
the models become more adept at capturing essential structural information
while reducing reliance on superficial characteristics that may introduce biases
or hinder generalization capabilities