Understanding Deep Representation Learning via Layerwise Feature Compression and Discrimination
arXiv:2311.02960v5 Announce Type: replace Abstract: Over the past decade, deep learning has proven to be a highly effective tool for learning meaningful features from raw data. However, it remains an open question how deep networks perform hierarchical feature learning across layers. In this work, we attempt to unveil this mystery by investigating the structures of intermediate features. Motivated by our empirical findings that linear layers mimic the roles of deep layers in nonlinear networks for feature learning, we explore how deep linear networks transform input data into output by investi
This paper represents a continuing effort in the AI research community to demystify deep learning mechanisms, driven by the increasing deployment and complexity of AI systems.
Understanding how deep networks learn features is critical for developing more efficient, robust, and interpretability-focused AI models, impacting a wide range of applications from computer vision to autonomous agents.
This research contributes to a deeper theoretical understanding of deep learning's internal workings, which could lead to more principled architectural designs and training methodologies rather than purely empirical approaches.
- · AI researchers
- · Deep learning framework developers
- · Industries relying on AI model optimization
- · Developers of opaque black-box AI systems
- · Those relying solely on empirical trial-and-error in AI design
Improved theoretical understanding of deep neural networks' feature learning process.
Development of more architecturally efficient and interpretable deep learning models.
Accelerated and more reliable deployment of advanced AI systems across critical sectors due to enhanced trust and performance guarantees.
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Read at arXiv cs.LG