Author name cluster

Brandon Trabucco

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8 papers

2 author rows

ICLR Conference 2025 Conference Paper

Wayward Concepts In Multimodal Models

Brandon Trabucco
Max Gurinas
Kyle Doherty
Ruslan Salakhutdinov

Large multimodal models such as Stable Diffusion can generate, detect, and classify new visual concepts after optimizing just the prompt. How are prompt embeddings for visual concepts found by prompt tuning methods different from typical discrete prompts? We conduct a large-scale analysis on three state-of-the-art models in text-to-image generation, open-set object detection, and zero-shot classification, and find that prompts optimized to represent new visual concepts are akin to an adversarial attack on the text encoder. Across 4,800 new embeddings trained for 40 diverse visual concepts on four standard datasets, we find perturbations within an $\epsilon$-ball to any prompt that reprogram models to generate, detect, and classify arbitrary subjects. These perturbations target the final-layers in text encoders, and steer pooling tokens towards the subject. We explore the transferability of these prompts, and find that perturbations reprogramming multimodal models are initialization-specific, and model-specific. Code for reproducing our work is available at the following site: https://wayward-concepts.github.io.

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ICLR Conference 2024 Conference Paper

Effective Data Augmentation With Diffusion Models

Brandon Trabucco
Kyle Doherty
Max Gurinas
Ruslan Salakhutdinov

Data augmentation is one of the most prevalent tools in deep learning, underpinning many recent advances, including those from classification, generative models, and representation learning. The standard approach to data augmentation combines simple transformations like rotations and flips to generate new images from existing ones. However, these new images lack diversity along key semantic axes present in the data. Current augmentations cannot alter the high-level semantic attributes, such as animal species present in a scene, to enhance the diversity of data. We address the lack of diversity in data augmentation with image-to-image transformations parameterized by pre-trained text-to-image diffusion models. Our method edits images to change their semantics using an off-the-shelf diffusion model, and generalizes to novel visual concepts from a few labelled examples. We evaluate our approach on few-shot image classification tasks, and on a real-world weed recognition task, and observe an improvement in accuracy in tested domains.

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NeurIPS Conference 2024 Conference Paper

Stylus: Automatic Adapter Selection for Diffusion Models

Michael Luo
Justin Wong
Brandon Trabucco
Yanping Huang
Joseph E. Gonzalez
Zhifeng Chen
Ruslan Salakhutdinov
Ion Stoica

Beyond scaling base models with more data or parameters, fine-tuned adapters provide an alternative way to generate high fidelity, custom images at reduced costs. As such, adapters have been widely adopted by open-source communities, accumulating a database of over 100K adapters—most of which are highly customized with insufficient descriptions. To generate high quality images, this paper explores the problem of matching the prompt to a Stylus of relevant adapters, built on recent work that highlight the performance gains of composing adapters. We introduce Stylus, which efficiently selects and automatically composes task-specific adapters based on a prompt's keywords. Stylus outlines a three-stage approach that first summarizes adapters with improved descriptions and embeddings, retrieves relevant adapters, and then further assembles adapters based on prompts' keywords by checking how well they fit the prompt. To evaluate Stylus, we developed StylusDocs, a curated dataset featuring 75K adapters with pre-computed adapter embeddings. In our evaluation on popular Stable Diffusion checkpoints, Stylus achieves greater CLIP/FID Pareto efficiency and is twice as preferred, with humans and multimodal models as evaluators, over the base model.

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ICLR Conference 2023 Conference Paper

A Simple Approach for Visual Room Rearrangement: 3D Mapping and Semantic Search

Brandon Trabucco
Gunnar A. Sigurdsson
Robinson Piramuthu
Gaurav S. Sukhatme
Ruslan Salakhutdinov

Physically rearranging objects is an important capability for embodied agents. Visual room rearrangement evaluates an agent's ability to rearrange objects in a room to a desired goal based solely on visual input. We propose a simple yet effective method for this problem: (1) search for and map which objects need to be rearranged, and (2) rearrange each object until the task is complete. Our approach consists of an off-the-shelf semantic segmentation model, voxel-based semantic map, and semantic search policy to efficiently find objects that need to be rearranged. Our method was the winning submission to the AI2-THOR Rearrangement Challenge in the 2022 Embodied AI Workshop at CVPR 2022, and improves on current state-of-the-art end-to-end reinforcement learning-based methods that learn visual room rearrangement policies from 0.53% correct rearrangement to 16.56%, using only 2.7% as many samples from the environment.

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ICML Conference 2022 Conference Paper

AnyMorph: Learning Transferable Polices By Inferring Agent Morphology

Brandon Trabucco
Mariano Phielipp
Glen Berseth

The prototypical approach to reinforcement learning involves training policies tailored to a particular agent from scratch for every new morphology. Recent work aims to eliminate the re-training of policies by investigating whether a morphology-agnostic policy, trained on a diverse set of agents with similar task objectives, can be transferred to new agents with unseen morphologies without re-training. This is a challenging problem that required previous approaches to use hand-designed descriptions of the new agent’s morphology. Instead of hand-designing this description, we propose a data-driven method that learns a representation of morphology directly from the reinforcement learning objective. Ours is the first reinforcement learning algorithm that can train a policy to generalize to new agent morphologies without requiring a description of the agent’s morphology in advance. We evaluate our approach on the standard benchmark for agent-agnostic control, and improve over the current state of the art in zero-shot generalization to new agents. Importantly, our method attains good performance without an explicit description of morphology.

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ICML Conference 2022 Conference Paper

Design-Bench: Benchmarks for Data-Driven Offline Model-Based Optimization

Brandon Trabucco
Xinyang Geng
Aviral Kumar
Sergey Levine

Black-box model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function, are ubiquitous in a wide range of domains, such as the design of proteins, DNA sequences, aircraft, and robots. Solving model-based optimization problems typically requires actively querying the unknown objective function on design proposals, which means physically building the candidate molecule, aircraft, or robot, testing it, and storing the result. This process can be expensive and time consuming, and one might instead prefer to optimize for the best design using only the data one already has. This setting—called offline MBO—poses substantial and different algorithmic challenges than more commonly studied online techniques. A number of recent works have demonstrated success with offline MBO for high-dimensional optimization problems using high-capacity deep neural networks. However, the lack of standardized benchmarks in this emerging field is making progress difficult to track. To address this, we present Design-Bench, a benchmark for offline MBO with a unified evaluation protocol and reference implementations of recent methods. Our benchmark includes a suite of diverse and realistic tasks derived from real-world optimization problems in biology, materials science, and robotics that present distinct challenges for offline MBO. Our benchmark and reference implementations are released at github. com/rail-berkeley/design-bench and github. com/rail-berkeley/design-baselines.

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ICML Conference 2021 Conference Paper

Conservative Objective Models for Effective Offline Model-Based Optimization

Brandon Trabucco
Aviral Kumar
Xinyang Geng
Sergey Levine

In this paper, we aim to solve data-driven model-based optimization (MBO) problems, where the goal is to find a design input that maximizes an unknown objective function provided access to only a static dataset of inputs and their corresponding objective values. Such data-driven optimization procedures are the only practical methods in many real-world domains where active data collection is expensive (e. g. , when optimizing over proteins) or dangerous (e. g. , when optimizing over aircraft designs, actively evaluating malformed aircraft designs is unsafe). Typical methods for MBO that optimize the input against a learned model of the unknown score function are affected by erroneous overestimation in the learned model caused due to distributional shift, that drives the optimizer to low-scoring or invalid inputs. To overcome this, we propose conservative objective models (COMs), a method that learns a model of the objective function which lower bounds the actual value of the ground-truth objective on out-of-distribution inputs and uses it for optimization. In practice, COMs outperform a number existing methods on a wide range of MBO problems, including optimizing controller parameters, robot morphologies, and superconducting materials.

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ICLR Conference 2021 Conference Paper

Discovering Non-monotonic Autoregressive Orderings with Variational Inference

Xuanlin Li
Brandon Trabucco
Dong Huk Park
Michael Luo
Sheng Shen 0001
Trevor Darrell
Yang Gao 0029

The predominant approach for language modeling is to encode a sequence of tokens from left to right, but this eliminates a source of information: the order by which the sequence was naturally generated. One strategy to recover this information is to decode both the content and ordering of tokens. Some prior work supervises content and ordering with hand-designed loss functions to encourage specific orders or bootstraps from a predefined ordering. These approaches require domain-specific insight. Other prior work searches over valid insertion operations that lead to ground truth sequences during training, which has high time complexity and cannot be efficiently parallelized. We address these limitations with an unsupervised learner that can be trained in a fully-parallelizable manner to discover high-quality autoregressive orders in a data driven way without a domain-specific prior. The learner is a neural network that performs variational inference with the autoregressive ordering as a latent variable. Since the corresponding variational lower bound is not differentiable, we develop a practical algorithm for end-to-end optimization using policy gradients. Strong empirical results with our solution on sequence modeling tasks suggest that our algorithm is capable of discovering various autoregressive orders for different sequences that are competitive with or even better than fixed orders.

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