5 research outputs found
Leveraging Diffusion Disentangled Representations to Mitigate Shortcuts in Underspecified Visual Tasks
Spurious correlations in the data, where multiple cues are predictive of the
target labels, often lead to shortcut learning phenomena, where a model may
rely on erroneous, easy-to-learn, cues while ignoring reliable ones. In this
work, we propose an ensemble diversification framework exploiting the
generation of synthetic counterfactuals using Diffusion Probabilistic Models
(DPMs). We discover that DPMs have the inherent capability to represent
multiple visual cues independently, even when they are largely correlated in
the training data. We leverage this characteristic to encourage model diversity
and empirically show the efficacy of the approach with respect to several
diversification objectives. We show that diffusion-guided diversification can
lead models to avert attention from shortcut cues, achieving ensemble diversity
performance comparable to previous methods requiring additional data
collection.Comment: Accepted at Neural Information Processing Systems(NeurIPS) 2023 -
Workshop on Diffusion Model
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA. The challenge attracted 16 teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA. The challenge attracted 16 teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings
ICLR 2021 Challenge for Computational Geometry & Topology: Design and Results
This paper presents the computational challenge on differential geometry and topology that happened within the ICLR 2021 workshop "Geometric and Topological Representation Learning". The competition asked participants to provide creative contributions to the fields of computational geometry and topology through the open-source repositories Geomstats and Giotto-TDA. The challenge attracted 16 teams in its two month duration. This paper describes the design of the challenge and summarizes its main findings