Non-Robust Features are Not Always Useful in One-Class Classification

The robustness of machine learning models has been questioned by the existence of adversarial examples. We examine the threat of adversarial examples in practical applications that require lightweight models for one-class classification. Building on Ilyas et al. (2019), we investigate the vulnerability of lightweight one-class classifiers to adversarial attacks and possible reasons for it. Our results show that lightweight one-class classifiers learn features that are not robust (e.g. texture) under stronger attacks. However, unlike in multi-class classification (Ilyas et al., 2019), these non-robust features are not always useful for the one-class task, suggesting that learning these unpredictive and non-robust features is an unwanted consequence of training.

ClickDiffusion: Harnessing LLMs for Interactive Precise Image Editing

Recently, researchers have proposed powerful systems for generating and manipulating images using natural language instructions. However, it is difficult to precisely specify many common classes of image transformations with text alone. For example, a user may wish to change the location and breed of a particular dog in an image with several similar dogs. This task is quite difficult with natural language alone, and would require a user to write a laboriously complex prompt that both disambiguates the target dog and describes the destination. We propose ClickDiffusion, a system for precise image manipulation and generation that combines natural language instructions with visual feedback provided by the user through a direct manipulation interface. We demonstrate that by serializing both an image and a multi-modal instruction into a textual representation it is possible to leverage LLMs to perform precise transformations of the layout and appearance of an image. Code available at https://github.com/poloclub/ClickDiffusion.

LLM Attributor: Interactive Visual Attribution for LLM Generation

While large language models (LLMs) have shown remarkable capability to generate convincing text across diverse domains, concerns around its potential risks have highlighted the importance of understanding the rationale behind text generation. We present LLM Attributor, a Python library that provides interactive visualizations for training data attribution of an LLM's text generation. Our library offers a new way to quickly attribute an LLM's text generation to training data points to inspect model behaviors, enhance its trustworthiness, and compare model-generated text with user-provided text. We describe the visual and interactive design of our tool and highlight usage scenarios for LLaMA2 models fine-tuned with two different datasets: online articles about recent disasters and finance-related question-answer pairs. Thanks to LLM Attributor's broad support for computational notebooks, users can easily integrate it into their workflow to interactively visualize attributions of their models. For easier access and extensibility, we open-source LLM Attributor at https://github.com/poloclub/ LLM-Attribution. The video demo is available at https://youtu.be/mIG2MDQKQxM.

Point and Instruct: Enabling Precise Image Editing by Unifying Direct Manipulation and Text Instructions

Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for users to specify precise image transformations with text alone. For example, in an image with several dogs, it is difficult to select a particular dog and move it to a precise location. Doing this with text alone would require a complex prompt that disambiguates the target dog and describes the destination. However, direct manipulation is well suited to visual tasks like selecting objects and specifying locations. We introduce Point and Instruct, a system for seamlessly combining familiar direct manipulation and textual instructions to enable precise image manipulation. With our system, a user can visually mark objects and locations, and reference them in textual instructions. This allows users to benefit from both the visual descriptiveness of natural language and the spatial precision of direct manipulation.

Mobile Fitting Room: On-device Virtual Try-on via Diffusion Models

The growing digital landscape of fashion e-commerce calls for interactive and user-friendly interfaces for virtually trying on clothes. Traditional try-on methods grapple with challenges in adapting to diverse backgrounds, poses, and subjects. While newer methods, utilizing the recent advances of diffusion models, have achieved higher-quality image generation, the human-centered dimensions of mobile interface delivery and privacy concerns remain largely unexplored. We present Mobile Fitting Room, the first on-device diffusion-based virtual try-on system. To address multiple inter-related technical challenges such as high-quality garment placement and model compression for mobile devices, we present a novel technical pipeline and an interface design that enables privacy preservation and user customization. A usage scenario highlights how our tool can provide a seamless, interactive virtual try-on experience for customers and provide a valuable service for fashion e-commerce businesses.

ObjectComposer: Consistent Generation of Multiple Objects Without Fine-tuning

Recent text-to-image generative models can generate high-fidelity images from text prompts. However, these models struggle to consistently generate the same objects in different contexts with the same appearance. Consistent object generation is important to many downstream tasks like generating comic book illustrations with consistent characters and setting. Numerous approaches attempt to solve this problem by extending the vocabulary of diffusion models through fine-tuning. However, even lightweight fine-tuning approaches can be prohibitively expensive to run at scale and in real-time. We introduce a method called ObjectComposer for generating compositions of multiple objects that resemble user-specified images. Our approach is training-free, leveraging the abilities of preexisting models. We build upon the recent BLIP-Diffusion model, which can generate images of single objects specified by reference images. ObjectComposer enables the consistent generation of compositions containing multiple specific objects simultaneously, all without modifying the weights of the underlying models.

LLM Self Defense: By Self Examination, LLMs Know They Are Being Tricked

Large language models (LLMs) have skyrocketed in popularity in recent years due to their ability to generate high-quality text in response to human prompting. However, these models have been shown to have the potential to generate harmful content in response to user prompting (e.g., giving users instructions on how to commit crimes). There has been a focus in the literature on mitigating these risks, through methods like aligning models with human values through reinforcement learning. However, it has been shown that even aligned language models are susceptible to adversarial attacks that bypass their restrictions on generating harmful text. We propose a simple approach to defending against these attacks by having a large language model filter its own responses. Our current results show that even if a model is not fine-tuned to be aligned with human values, it is possible to stop it from presenting harmful content to users by validating the content using a language model.

ManimML: Communicating Machine Learning Architectures with Animation

There has been an explosion in interest in machine learning (ML) in recent years due to its applications to science and engineering. However, as ML techniques have advanced, tools for explaining and visualizing novel ML algorithms have lagged behind. Animation has been shown to be a powerful tool for making engaging visualizations of systems that dynamically change over time, which makes it well suited to the task of communicating ML algorithms. However, the current approach to animating ML algorithms is to handcraft applications that highlight specific algorithms or use complex generalized animation software. We developed ManimML, an open-source Python library for easily generating animations of ML algorithms directly from code. We sought to leverage ML practitioners' preexisting knowledge of programming rather than requiring them to learn complex animation software. ManimML has a familiar syntax for specifying neural networks that mimics popular deep learning frameworks like Pytorch. A user can take a preexisting neural network architecture and easily write a specification for an animation in ManimML, which will then automatically compose animations for different components of the system into a final animation of the entire neural network. ManimML is open source and available at https://github.com/helblazer811/ManimML.

Manifold Contrastive Learning with Variational Lie Group Operators

Self-supervised learning of deep neural networks has become a prevalent paradigm for learning representations that transfer to a variety of downstream tasks. Similar to proposed models of the ventral stream of biological vision, it is observed that these networks lead to a separation of category manifolds in the representations of the penultimate layer. Although this observation matches the manifold hypothesis of representation learning, current self-supervised approaches are limited in their ability to explicitly model this manifold. Indeed, current approaches often only apply augmentations from a pre-specified set of "positive pairs" during learning. In this work, we propose a contrastive learning approach that directly models the latent manifold using Lie group operators parameterized by coefficients with a sparsity-promoting prior. A variational distribution over these coefficients provides a generative model of the manifold, with samples which provide feature augmentations applicable both during contrastive training and downstream tasks. Additionally, learned coefficient distributions provide a quantification of which transformations are most likely at each point on the manifold while preserving identity. We demonstrate benefits in self-supervised benchmarks for image datasets, as well as a downstream semi-supervised task. In the former case, we demonstrate that the proposed methods can effectively apply manifold feature augmentations and improve learning both with and without a projection head. In the latter case, we demonstrate that feature augmentations sampled from learned Lie group operators can improve classification performance when using few labels.

PrefGen: Preference Guided Image Generation with Relative Attributes

Deep generative models have the capacity to render high fidelity images of content like human faces. Recently, there has been substantial progress in conditionally generating images with specific quantitative attributes, like the emotion conveyed by one's face. These methods typically require a user to explicitly quantify the desired intensity of a visual attribute. A limitation of this method is that many attributes, like how "angry" a human face looks, are difficult for a user to precisely quantify. However, a user would be able to reliably say which of two faces seems "angrier". Following this premise, we develop the $\textit{PrefGen}$ system, which allows users to control the relative attributes of generated images by presenting them with simple paired comparison queries of the form "do you prefer image $a$ or image $b$?" Using information from a sequence of query responses, we can estimate user preferences over a set of image attributes and perform preference-guided image editing and generation. Furthermore, to make preference localization feasible and efficient, we apply an active query selection strategy. We demonstrate the success of this approach using a StyleGAN2 generator on the task of human face editing. Additionally, we demonstrate how our approach can be combined with CLIP, allowing a user to edit the relative intensity of attributes specified by text prompts. Code at https://github.com/helblazer811/PrefGen.