Statistical Surgery: Compressing VGG19
How we aggressively reduced deep learning parameters in hematological imaging while maintaining 98.4% accuracy.
Introduction
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74.5% reduction in total parameters via structured surgery.
58.1% faster Wall-clock time on standard hardware (T4 GPU).
Significant reduction in serialized model size for edge deployment.
§1 منهجية البحث
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Hematological Dataset Exploration
Dataset N = 17,092 Samples
Statistical Bias Note
"The moderate class imbalance observed here necessitates the use of Macro-averaged F1 metrics. Accuracy alone would be biased toward the majority classes (Neutrophils and Eosinophils)."
Intensity Profile
§2 تحليل النموذج الأساسي
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VGG19 Hierarchical Construction
Isometric decomposition visualizing the bottleneck transitions and parameter distribution.
Select an architectural block to inspect its hierarchical role and computational complexity.
§ 2.1
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PCA Redundancy Audit
Quantifying the intrinsic dimensionality of latent activation spaces.
Select a layer to visualize the variance decay and identify the representational 'elbow'.
§3 L1 Lasso Regularization
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Training Dynamics & Phase Transition
Tracking the emergence of sparsity under increasing L1 pressure.
Weight Magnitude Audit
Analyzing the post-Lasso zero-attraction topology.
Parameters that can be numerically zeroed without significant loss impact.
Static occupancy despite numerical sparsity—demonstrating the Hardware Paradox.
Unstructured sparsity does not bypass SIMD multipliers. The tensor must undergo structural surgery to gain latency benefits.
§4 Structured Surgery via L0 Gates
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The Gaussian Stochastic Gate
Differentiable L0 relaxation: Visualizing the transition from continuous parameters to discrete hardware gates.
Each cell represents a convolutional channel. Stochastic sampling determines whether the "shutter" (gate) is physically open for inference.
Identity Map Inherited
The Gaussian gate acts as a continuous proxy for discrete L0 penalization. By adjusting the bias, we modulate the probability of channel survival.
§ 4.2
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The Survival Gradient
Visualizing the structural survival of VGG19 channels. Early layers (Blocks 1-2) are preserved for low-level feature extraction, while deep layers are aggressively pruned.
§ 4.3
Pruning Visualization
Discarding redundant channels physically to unlock hardware throughput.
§ 4.4
Throughput Benchmark
Simulating a clinical queue of 20 diagnostic batches. The L0 model achieves physical acceleration through tensor surgery, clearing the queue while the Baseline still processes.
§5 Low-Rank Factorization (SVD)
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Singular Value Decomposition
Decomposing high-density weight tensors into essential geometric primitives.
§ 5.1
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Diagnostic Fidelity Sweep
Why SVD works: Clinical images contain massive spatial redundancy.
The previous section proved that weights are redundant. This section proves that the diagnostic data itselfis low-rank, allowing the network to discard high-frequency "noise" without losing the cell's nucleus structure.
Optimal Spectral Cutoff. High-frequency pixel noise is removed, but the diagnostic nucleus remains structurally intact.
Spectral Rank Profile
§6 Discussion & Unified Synthesis
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Statistical Parsimony Audit
Evaluating model selection through Information-Theoretic criteria. Lower values indicate a more efficient trade-off between empirical fit and parameter complexity.
Metric Definitions
Penalize complexity to prevent overfitting. BIC imposes a stronger penalty based on sample size, favouring simpler models.
Minimum Description Length. Evaluates the statistical hypothesis by the length of its shortest possible description.
The massive reduction in AIC/BIC/MDL confirms that VGG19 is severely over-parameterized for the BloodMNIST task, captured here by the Parsimony Gap.
§ 6.1
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The Pareto Efficiency Frontier
Mapping the trade-off between predictive fidelity and resource constraints.
§ 6.2
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Heterogeneous Degradation Audit
Visualizing the relative performance drop across compression variants compared to the uncompressed baseline. Structurally distinctive classes remain stable, whereas morphologically ambiguous classes account for the majority of the degradation.
Select a matrix cell to view class-specific stability metrics.
The 85% Guardrail: No class fell below the predetermined clinical threshold, confirming that even the most aggressive surgery preserved the minimum features required for diagnostic reliability.
§ 6.3
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Unified Error Topology Analysis
Visualizing the transition of classification boundaries under different compression constraints.
Boundary Sensitivity
Statistical Insight: The diagonal entries represent the true positives. Off-diagonal concentration in the middle rows confirms that morphological ambiguity is the primary bottleneck for both dense and sparse models.
§ 6.4
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Compounding Efficiency
Simulation of Pipeline Stacking
The Deployment Framework
A statistically-driven matrix for selecting the optimal compression strategy based on clinical and hardware constraints.
Primary Constraint Analysis
The Practitioner's Playbook
Summary of Research Recommendations
Match Method to Redundancy
Not all redundancy is created equal. Fully-connected layers exhibit high-rank linear redundancy, making them ideal for SVD. Convolutional layers, however, possess spatial filter redundancy that requires structured pruning.
Use SVD for dense layers; use L0 for convolutional bases.
Hardware Profiling Audit
System Audit & Reproducibility Specs