---
# System prepended metadata

title: Model Reprogramming
tags: [Progress]

---

###### tags: `Progress`
# Model Reprogramming

## Introduction

* Deep learning in **resource-limited** domains still faces the following challenges including (i)**limited data**, (ii) **constrained model development cost**, and (iii) **lack of adequate pre-trained models for effective finetuning**

* Model reprogramming enables resource-efficient cross-domain machine learning by **repurposing** and **reusing** **a well-developed pretrained model** from a source domain to solve tasks in a target domain without model finetuning.

![](https://i.imgur.com/VduAjDg.png)

<!-- Input transformation.......

Trainable Perturbation and Frozen Pretrained Model

Some data is sparse 

Adversarial Example -->

## Related work
<!-- ## Model Reprogramming: Resource-Efficient Cross-Domain Machine Learning -->

## Voice2Series: Reprogramming Acoustic Models for Time Series Classification
[Link](https://arxiv.org/pdf/2106.09296.pdf)


We propose Voice2Series (V2S), a novel end-to-end approach that **reprograms acoustic models for time series classification**, through input transformation learning and output label mapping



![](https://i.imgur.com/a47oq9e.png)

###  V2S Reprogramming on Data Inputs
Our V2S aims to find a **trainable input transformation function $\mathcal{H}$** that is universal to all target data inputs, which serves the purpose of reprogramming $x_t$ into the source data space $\mathcal{X}_{\mathcal{S}} \subseteq \mathbb{R}^{d_{\mathcal{S}}}$, where $d_{\mathcal{T}}<d_{\mathcal{S}}$.  Specifically, the reprogrammed sample $x_t^{\prime}$ is formulated as

$$
x_t^{\prime}=\mathcal{H}\left(x_t ; \theta\right):=\operatorname{Pad}\left(x_t\right)+\underbrace{M \odot \theta}_{\triangleq \delta}
$$


where Pad($x_t$) is a **zero padding function** that outputs a zero-padded time series of dimension $d_{\mathcal{S}}$. The term $M \in\{0,1\}^{d_s}$ is a **binary mask** that indicates the location of $x_t$  in its zero-padded input Pad($x_t$ ), where the i-th entry of M is 0 if xt is present (indicating the entry is non-reprogrammable), and it is 1 otherwise.

### V2S Reprogramming on Acoustic Models (AMs)

One can obtain the class prediction of the source model $f_{\mathcal{S}}$ on an reprogrammed target data sample $x_t$, denoted by

$$
P\left(y_s \mid f_{\mathcal{S}}\left(\mathcal{H}\left(x_t ; \theta\right)\right)\right) \text {, for all } y_s \in \mathcal{Y}_{\mathcal{S}}
$$

We assign a **(many-to-one) label mapping function $h$** to map source labels to target labels. For a target label $y_t \in \mathcal{Y}_{\mathcal{T}}$, its class prediction will be the averaged class predictions over the set of source labels assigned to it.

We use the term $P\left(h\left(\mathcal{Y}_{\mathcal{S}}\right) \mid f_{\mathcal{S}}\left(\mathcal{H}\left(x_t ; \theta\right)\right)\right)$ to denote the prediction probability of the target task on the associated ground-truth target label $y_t=h\left(\mathcal{Y}_{\mathcal{S}}\right)$. Finally, we learn the optimal parameters $\theta^*$ for data input reprogramming by optimizing the following objective:

$$
\theta^*=\arg \min _\theta \underbrace{-\log P\left(h\left(\mathcal{Y}_{\mathcal{S}}\right) \mid f_{\mathcal{S}}\left(\mathcal{H}\left(x_t ; \theta\right)\right)\right.}_{\text {V2S loss } \triangleq L} ;
$$

$$
\text { where } h\left(\mathcal{Y}_{\mathcal{S}}\right)=y_t
$$

![](https://i.imgur.com/9Tz6iRi.png)



## Cross-modal Adversarial Reprogramming
[Link](https://arxiv.org/pdf/2102.07325.pdf)


With the abundance of **large-scale deep learning models**, it has become possible to **repurpose pre-trained networks for new tasks.**

Recent works on adversarial reprogramming have shown that it is possible to **repurpose neural networks for alternate tasks without modifying the network   architecture or parameters.**

![](https://i.imgur.com/nitkaBg.png)

## Theoretical Characterization of Model Reprogramming

**Lemma 1:** Given a $K$-way neural network classifier $f(\cdot)=$ $\eta(z(\cdot))$. Let $\mu_z$ and $\mu_z^{\prime}$ be the **probability measures of the logit representations** $\{z(x)\}$ and $\left\{z\left(x^{\prime}\right)\right\}$ from two data domains $\mathcal{D}$ and $\mathcal{D}^{\prime}$, where $x \sim \mathcal{D}$ and $x^{\prime} \sim \mathcal{D}^{\prime}$. Assume independent draws for $x$ and $x^{\prime}$, i.e., $\Phi_{\mathcal{D}, \mathcal{D}^{\prime}}\left(x, x^{\prime}\right)=$ $\Phi_{\mathcal{D}}(x) \cdot \Phi_{\mathcal{D}^{\prime}}\left(x^{\prime}\right)$. Then

$$
\mathbb{E}_{x \sim \mathcal{D}, x^{\prime} \sim \mathcal{D}^{\prime}}\left\|f(x)-f\left(x^{\prime}\right)\right\|_2 \leq 2 \sqrt{K} \cdot \mathcal{W}_1\left(\mu_z, \mu_z^{\prime}\right)
$$

where $\mathcal{W}_1\left(\mu_z, \mu_z^{\prime}\right)$ is the Wasserstein-1 distance between $\mu_z$ and $\mu_z^{\prime}$.

With Lemma 1, we now state the main theorem regarding an upper bound on population risk for reprogramming.


**Theorem 1:** Let **$\delta^*$ denote the learned additive input transformation** for reprogramming. The population risk for the target task via reprogramming a $K$-way source neural network classifier $f_{\mathcal{S}}(\cdot)=\eta\left(z_{\mathcal{S}}(\cdot)\right)$, denoted by $\mathbb{E}_{\mathcal{D}_{\mathcal{T}}}\left[\ell_{\mathcal{T}}\left(x_t+\delta^*, y_t\right)\right]$, is upper bounded by

$$
\mathbb{E}_{\mathcal{D}_{\mathcal{T}}}\left[\ell_{\mathcal{T}}\left(x_t+\delta^*, y_t\right)\right] \leq \underbrace{\epsilon_{\mathcal{S}}}_{\text {source risk }}+2 \sqrt{K} \cdot \underbrace{\mathcal{W}_1\left(\mu\left(z_{\mathcal{S}}\left(x_t+\delta^*\right)\right), \mu\left(z_{\mathcal{S}}\left(x_s\right)\right)\right)_{x_t \sim \mathcal{D}_{\mathcal{T}}, x_s \sim \mathcal{D}_{\mathcal{S}}}}_{\text {representation alignment loss via reprogramming }}
$$

**The results suggest that reprogramming can attain better performance (lower risk) when the source model has a lower source loss and a smaller representation alignment loss.**

### Experiment


* Statistics of the datasets used for our reprogramming tasks. We also include the test accuracy of both neural network based and TF-IDF based benchmark classifiers trained from scratch on the train set
![](https://i.imgur.com/u3XAudW.png)


* Example outputs of our adversarial reprogramming function in both unbounded (top) and bounded (bottom) attack settings while reprogramming two different pre-trained image classifiers for a DNA sequence classification task (H3).

![](https://i.imgur.com/IiRCuqi.jpg)


## Summary and Takeaways

* The model reprogramming provide promising and new the state-of-the-art results on **low-resource data.**
* Trough trainable input noise, reprogramming requires **less trainable parameters** compared to model fine-tuning and training from scratch.
* The neural saliency analysis demonstrates that the frozen pre-trained model also learn to recognize the target signal and could be further advance in **cross-domain learning** (e.g., speech and biomedical; image and genome)



## My Idea
1. Maybe extend Model Reprogramming to Adversarial example.

2. Utilize **Model Reprogramming** on Adversarial Purification, since Train diffusion model from scratch is time-consuming and require computational power. Below are the flow of Adversarial Purification.
![](https://i.imgur.com/q32xcio.png)


<!-- 3. Low resource and different domain adaptation -->

