Flow to Learn: Flow Matching on Neural Network Parameters

Foundational language models show a remarkable ability to learn new concepts during inference via context data. However, similar work for images lag behind. To address this challenge, we introduce FLoWN, a flow matching model that learns to generate neural network parameters for different tasks. Our approach models the flow on latent space, while conditioning the process on context data. Experiments verify that FLoWN attains various desiderata for a meta-learning model. In addition, it matches or exceeds baselines on in-distribution tasks, provides better initializations for classifier training, and is performant on out-of-distribution few-shot tasks while having a fine-tuning mechanism to improve performance.

An Empirical Study of Aegis

Bit flipping attacks are one class of attacks on neural networks with numerous defense mechanisms invented to mitigate its potency. Due to the importance of ensuring the robustness of these defense mechanisms, we perform an empirical study on the Aegis framework. We evaluate the baseline mechanisms of Aegis on low-entropy data (MNIST), and we evaluate a pre-trained model with the mechanisms fine-tuned on MNIST. We also compare the use of data augmentation to the robustness training of Aegis, and how Aegis performs under other adversarial attacks, such as the generation of adversarial examples. We find that both the dynamic-exit strategy and robustness training of Aegis has some drawbacks. In particular, we see drops in accuracy when testing on perturbed data, and on adversarial examples, as compared to baselines. Moreover, we found that the dynamic exit-strategy loses its uniformity when tested on simpler datasets. The code for this project is available on GitHub.

Using Diffusion Models to Generate Synthetic Labelled Data for Medical Image Segmentation

In this paper, we proposed and evaluated a pipeline for generating synthetic labeled polyp images with the aim of augmenting automatic medical image segmentation models. In doing so, we explored the use of diffusion models to generate and style synthetic labeled data. The HyperKvasir dataset consisting of 1000 images of polyps in the human GI tract obtained from 2008 to 2016 during clinical endoscopies was used for training and testing. Furthermore, we did a qualitative expert review, and computed the Fr\'echet Inception Distance (FID) and Multi-Scale Structural Similarity (MS-SSIM) between the output images and the source images to evaluate our samples. To evaluate its augmentation potential, a segmentation model was trained with the synthetic data to compare their performance with the real data and previous Generative Adversarial Networks (GAN) methods. These models were evaluated using the Dice loss (DL) and Intersection over Union (IoU) score. Our pipeline generated images that more closely resembled real images according to the FID scores (GAN: $118.37 \pm 1.06 \text{ vs SD: } 65.99 \pm 0.37$). Improvements over GAN methods were seen on average when the segmenter was entirely trained (DL difference: $-0.0880 \pm 0.0170$, IoU difference: $0.0993 \pm 0.01493$) or augmented (DL difference: GAN $-0.1140 \pm 0.0900 \text{ vs SD }-0.1053 \pm 0.0981$, IoU difference: GAN $0.01533 \pm 0.03831 \text{ vs SD }0.0255 \pm 0.0454$) with synthetic data. Overall, we obtained more realistic synthetic images and improved segmentation model performance when fully or partially trained on synthetic data.