Variational Autoencoder-Based Synthetic Data Generation for Augmenting Mosquito Larvae Image Datasets
Abstract
We propose a convolutional Variational Autoencoder (VAE) to synthesize mosquito-larvae images for dataset augmentation. The dataset comprises 1,300 grayscale microscope images (640×480) resized to 224×224 and split 1,200/100 for train/test. The encoder uses four Conv2D blocks (32, 64, 128, 256) that project to a 100-dimensional latent space; the decoder mirrors this with Conv2DTranspose layers. Training uses Adam with an MSE+KL objective, batch size 64, over 100–400 epochs. Image realism is assessed via Fréchet Inception Distance (Inception-v3 features) across latent sizes {50, 100} and epoch counts. The best configuration (latent = 100, 400 epochs) achieves FID = 0.4668 with total loss in the 29,206–33,806 range, representing a 59.7% reduction relative to a 50-D/100-epoch baseline (FID = 1.1588). Among variants, the standard VAE yields the lowest FID overall, outperforming VQ-VAE and VAE-S, while β-VAE is competitive in lower-capacity settings. These results indicate that the learned generator produces samples statistically close to the real distribution and provide a practical training recipe for generating synthetic larvae imagery to support downstream recognition tasks.References
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