Modified Inception-V1 Network Overview

• Modified Inception-V1 network is an architectural variant that adapts GoogLeNet’s multi-branch design with reduced modules and channel counts for resource-constrained settings.
• Key modifications include streamlined inception blocks, elimination of auxiliary classifiers, and tailored input/output adjustments for tasks like biometrics and matrix regression.
• Enhanced regularization strategies, such as dropout and L2 weight decay, contribute to robust performance, achieving around 89% accuracy in specialized biometric authentication scenarios.

A modified Inception-V1 network refers to any architectural adaptation of the original GoogLeNet/Inception-V1 model, where significant changes have been introduced to support resource efficiency, novel input domains, or new problem formulations. Modifications span pruning of module counts, branch and filter-width adjustments, revised input/output interfaces, domain-optimized kernels, and alterations in regularization or loss setup. These variants appear throughout the literature, particularly in mobile-friendly biometrics, low-latency classification, matrix-structured regression, and restoration/segmentation tasks.

1. Architectural Foundations and Common Modifications

The original Inception-V1/GoogLeNet module is characterized by four parallel branches: a pure 1×1 convolution, 1×1→3×3, 1×1→5×5, and 3×3 max-pool→1×1 projection, each followed by ReLU activations and merged via concatenation along the channel axis. This canonical block is repeated up to nine times in the full architecture, with periodic spatial reduction layers, auxiliary classifier heads, and a multi-class softmax top.

Key modifications to the standard Inception-V1, demonstrated in specialized applied settings, typically include:

• Elimination of auxiliary classifiers to simplify optimization and reduce memory footprint.
• Reduction in the number of inception modules, frequently to three or less, reflecting smaller input sizes and lower data complexity (Balkhi et al., 14 Nov 2025).
• Uniform halving or even greater compression of branch channel widths, necessary for mobile or on-device deployment.
• Input interface adaptation, e.g., downscaling from 224×224×3 to 64×64×1 for grayscale biometrics (Balkhi et al., 14 Nov 2025), or to small configuration matrices for geometric regression (Erbin et al., 2020).
• Output interface contraction to binary softmax for authentication or single-neuron regression for geometric property estimation (Balkhi et al., 14 Nov 2025, Erbin et al., 2020).
• Regularization via increased dropout, L2 (or L1+L2) kernel penalty, and omission of data augmentation except where absolutely necessary.

These modifications are often justified by domain constraints: limited per-user data in biometrics, the redundancy of deep towers for sparse or structured inputs, or aggressive operation-count targets for live deployment.

2. Detailed Topology: Finger-Drawn Authentication Example

A representative instantiation is the network employed for finger-drawn digit-based authentication on mobile touchscreen devices (Balkhi et al., 14 Nov 2025). Its topology can be summarized as:

• Input: 64×64×1 grayscale image
• Initial stem: matches GoogLeNet structure but with channel depths halved; reduction to 28×28 spatial grid
• Inception modules: three sequential blocks, each with four branches:
  • 1×1 convolution (channel reduction)
  • 1×1→3×3 convolution
  • 1×1→5×5 convolution
  • 3×3 max-pool→1×1 projection
  • All use stride=1, appropriate padding, ReLU and BatchNorm.
• Channel counts in all branches are ~50% those of the canonical GoogLeNet (e.g., 32/64/16/16 per branch).
• After the third module: 3×3 stride-2 max-pooling, reducing to 14×14.
• One 3×3 "bottleneck" convolution, outputting 128 channels.
• GlobalAveragePooling to 1×1×128.
• Dropout(0.5), followed by a dense layer producing two outputs with softmax activation for authorized/unauthorized classification.

Per-user one-vs-all models are trained with Adam (lr=1e-3, decay at epoch 15); batch size 32. L2 kernel weight decay (1e-5) and dropout (0.5) provide regularization. No data augmentation beyond fixed 6-pixel stroke-width rendering is used.

3. Departures from Canonical Inception-V1

Modifications to the original architecture in the context of mobile/small-sample or non-vision matrix domains are universal across specialized applications:

Change	Motivation	Observed Effect
Fewer inception modules (e.g. 3)	Lower computational load	Small loss, sometimes no loss, in accuracy
Reduced channel counts	Shrink memory/compute footprint	Maintains performance when input is low-res or information-dense
No auxiliary classifiers	Stability, simplicity	Reduces overfitting in small-data regimes
Downscaled input	Matches data source	Higher saliency, avoids overfitting
Dropout/weight decay increased	Control overfitting	Robust performance with intra-class variation
Minimized data augmentation	Domain-determined	Irrelevant or harmful on consistent/controlled inputs

No new branches are introduced; the four-branch multi-scale filter layout remains, preserving the multi-scale feature extraction capability that is critical for distinguishing local from global patterns in finger-drawn biometrics (Balkhi et al., 14 Nov 2025).

4. Training Regimes and Regularization Strategies

Modified Inception-V1 variants adopt regularization aligned with small dataset sizes and high intra-class variability:

• Dropout of 0.5 after global pooling mitigates overfitting, particularly acute given that only 1,200 per-user training samples are available.
• L2 weight decay is applied on all parameterized layers (Balkhi et al., 14 Nov 2025).
• Adam optimizer is frequently used for its adaptive step-size properties.
• Early stopping is triggered after 5 epochs without improvement in validation loss; the total epoch limit is 50 for the biometric authentication case.

These strategies collectively reflect an architectural philosophy geared toward balanced capacity and efficient convergence under hardware and data constraints.

5. Performance Metrics and Comparative Analysis

The performance of the modified Inception-V1 for authentication is quantified across 20 users (Balkhi et al., 14 Nov 2025):

Metric	Value (mean across users)
Accuracy (ACC)	88.6%
False Acceptance Rate	12.8%
False Rejection Rate	8.9%
Equal Error Rate (EER)	10.9%
AUC	0.9562

Compared to a lightweight six-layer CNN, the modified Inception-V1 achieves similar accuracy (~89%) but has more than double the parameter count (1.9M vs 0.75M). This suggests that, for finger-drawn biometrics at 64×64×1 input, deep, multi-scale models retain a small premium in discriminative power, though with diminishing returns as model size increases in resource-constrained deployments.

6. Rationale for Modification and Broader Context

The rationale for these architectural changes is multifaceted (Balkhi et al., 14 Nov 2025):

• Drastic reduction in module count and per-branch width is driven by mobile deployment requirements.
• Finger-drawn digit inputs are highly salient by design (thicker strokes, small canvas), rendering most of the depth of canonical Inception-V1 unnecessary.
• Maintaining three inception modules preserves critical multi-scale pattern recognition (e.g., for distinguishing loops vs. straight strokes).
• Elimination of auxiliary heads aligns with reduced overfitting risk in lower-data, less complex domains.
• Dropout and weight decay explicitly target intra-class variability and overfitting stemming from human-drawn input heterogeneity.

The resulting network achieves practical real-time performance on smartphones, matches or exceeds the accuracy of signature verification models with far fewer parameters, and demonstrates the continuing utility of the Inception architectural motif in resource-limited, application-driven settings (Balkhi et al., 14 Nov 2025).

7. Variants in Broader Literature

Multiple research groups have advanced distinct modified Inception-V1 networks, often targeting non-visual or highly structured domains—in all cases, simplifying or domain-adapting the base architecture:

• For Complete Intersection Calabi-Yau 3-fold regression, only two 1D convolution branches (sweeping rows and columns respectively) are used; pooling and auxiliary outputs are omitted (Erbin et al., 2020).
• In traffic sign classification, an additional (fifth) branch is added after the pool path to enhance local detail capture, in conjunction with spatial transformer layers (Haloi, 2015).
• For pixel-wise restoration, the pooling branch is removed and a 7×7 conv branch added, producing a lightweight, fully convolutional design for tasks such as skin detection, semantic segmentation, and artifact reduction (Kim et al., 2017).

These demonstrate the versatility of the Inception-V1 paradigm: the base multi-branch motif provides a foundation for efficient multi-scale feature extraction, while the modular structure facilitates insertion, removal, or adaptation of branches, depths, and heads to the demands of new domains and hardware.

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References:

Neural Network-Powered Finger-Drawn Biometric Authentication (Balkhi et al., 14 Nov 2025) Inception Neural Network for Complete Intersection Calabi-Yau 3-folds (Erbin et al., 2020) Traffic Sign Classification Using Deep Inception Based Convolutional Networks (Haloi, 2015) A New Convolutional Network-in-Network Structure and Its Applications in Skin Detection, Semantic Segmentation, and Artifact Reduction (Kim et al., 2017)