no code implementations • 27 Mar 2024 • Aditya Golatkar, Alessandro Achille, Luca Zancato, Yu-Xiang Wang, Ashwin Swaminathan, Stefano Soatto
To reduce risks of leaking private information contained in the retrieved set, we introduce Copy-Protected generation with Retrieval (CPR), a new method for RAG with strong copyright protection guarantees in a mixed-private setting for diffusion models. CPR allows to condition the output of diffusion models on a set of retrieved images, while also guaranteeing that unique identifiable information about those example is not exposed in the generated outputs.
no code implementations • 20 Mar 2024 • Alessandro Favero, Luca Zancato, Matthew Trager, Siddharth Choudhary, Pramuditha Perera, Alessandro Achille, Ashwin Swaminathan, Stefano Soatto
In particular, we show that as more tokens are generated, the reliance on the visual prompt decreases, and this behavior strongly correlates with the emergence of hallucinations.
no code implementations • 3 Nov 2023 • Abdelhak Lemkhenter, Manchen Wang, Luca Zancato, Gurumurthy Swaminathan, Paolo Favaro, Davide Modolo
We show that SemiGPC improves performance when paired with different Semi-Supervised methods such as FixMatch, ReMixMatch, SimMatch and FreeMatch and different pre-training strategies including MSN and Dino.
1 code implementation • 23 Oct 2023 • Tian Yu Liu, Matthew Trager, Alessandro Achille, Pramuditha Perera, Luca Zancato, Stefano Soatto
We propose to extract meaning representations from autoregressive language models by considering the distribution of all possible trajectories extending an input text.
no code implementations • 1 Jun 2023 • Pramuditha Perera, Matthew Trager, Luca Zancato, Alessandro Achille, Stefano Soatto
We investigate whether prompts learned independently for different tasks can be later combined through prompt algebra to obtain a model that supports composition of tasks.
no code implementations • NeurIPS 2023 • Marco Fumero, Florian Wenzel, Luca Zancato, Alessandro Achille, Emanuele Rodolà, Stefano Soatto, Bernhard Schölkopf, Francesco Locatello
Recovering the latent factors of variation of high dimensional data has so far focused on simple synthetic settings.
no code implementations • CVPR 2023 • Luca Zancato, Alessandro Achille, Tian Yu Liu, Matthew Trager, Pramuditha Perera, Stefano Soatto
Second, we apply ${\rm T^3AR}$ for test-time adaptation and show that exploiting a pool of external images at test-time leads to more robust representations over existing methods on DomainNet-126 and VISDA-C, especially when few adaptation data are available (up to 8%).
no code implementations • ICCV 2023 • Matthew Trager, Pramuditha Perera, Luca Zancato, Alessandro Achille, Parminder Bhatia, Stefano Soatto
These vectors can be seen as "ideal words" for generating concepts directly within the embedding space of the model.
no code implementations • 15 Feb 2023 • Benjamin Bowman, Alessandro Achille, Luca Zancato, Matthew Trager, Pramuditha Perera, Giovanni Paolini, Stefano Soatto
During inference, models can be assembled based on arbitrary selections of data sources, which we call "\`a-la-carte learning".
no code implementations • CVPR 2023 • Benjamin Bowman, Alessandro Achille, Luca Zancato, Matthew Trager, Pramuditha Perera, Giovanni Paolini, Stefano Soatto
During inference, models can be assembled based on arbitrary selections of data sources, which we call a-la-carte learning.
no code implementations • 29 Sep 2021 • Luca Zancato, Alessandro Achille, Giovanni Paolini, Alessandro Chiuso, Stefano Soatto
After modeling the signals, we use an anomaly detection system based on the classic CUMSUM algorithm and a variational approximation of the $f$-divergence to detect both isolated point anomalies and change-points in statistics of the signals.
no code implementations • 6 Jun 2021 • Luca Zancato, Alessandro Chiuso
We present a novel Deep Neural Network (DNN) architecture for non-linear system identification.
no code implementations • 29 Jan 2021 • Aditya Deshpande, Alessandro Achille, Avinash Ravichandran, Hao Li, Luca Zancato, Charless Fowlkes, Rahul Bhotika, Stefano Soatto, Pietro Perona
Since all model selection algorithms in the literature have been tested on different use-cases and never compared directly, we introduce a new comprehensive benchmark for model selection comprising of: i) A model zoo of single and multi-domain models, and ii) Many target tasks.
no code implementations • NeurIPS 2020 • Luca Zancato, Alessandro Achille, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto
We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function.