[CMU AI Seminar] April 13 (Today!) at 12pm (GHC 6115 & Zoom) -- Mazda Moayeri (UMD) -- Turning Models into Super Models without Supersizing: Making the most of what we already have -- AI Seminar sponsored by SambaNova Systems

[Asher Trockman]

Dear all,

We look forward to seeing you* today, **this Thursday (4/13)* from
*1**2:00-1:00
PM (U.S. Eastern time)* for the next talk of this semester's
*CMU AI Seminar*, sponsored by SambaNova Systems <https://sambanova.ai/>.
The seminar will be held in GHC 6115 *with pizza provided *and will be
streamed on Zoom. *Note: the speaker will be remote.*

To learn more about the seminar series or to see the future schedule,
please visit the seminar website <http://www.cs.cmu.edu/~aiseminar/>.

Today (4/13), *Mazda Moayeri* (UMD) will be giving a talk titled *"**Turning
Models into Super Models without Supersizing: Making the most of what we
already have**".*

*Title*: Turning Models into Super Models without Supersizing: Making the
most of what we already have

*Talk Abstract*: Newer, larger vision models trained on more data are
released nearly every week. However, many critical issues persist, such as
poor interpretability, robustness, and fairness. Today, we ask, how can we
better use the models and data we already have to tackle these issues?
First, we propose a method to organize data for improved spurious
correlation robustness: We utilize an adversarially trained model to
discover spurious features that models rely upon, and scalably measure the
presence of these spurious cues (i.e. spuriosity) per image. After ranking
images by spuriosity, we can very easily measure and mitigate bias caused
by spurious correlations, all without needing new data. We demonstrate the
feasibility of our framework on ImageNet, resulting in a massive dataset (
salient-imagenet.cs.umd.edu) answering the question, “what reasons do deep
models use to solve ImageNet classification?”. Next, we show how existing
models can work together with minimal additional training. Namely, we
present a method for accessing the feature space of an off-the-shelf vision
models directly with text, extending multimodal (i.e. CLIP) capabilities to
smaller unimodal models trained with far less data and supervision. Our
method unlocks many new powers (especially for interpretability) of
existing models, all without ever needing to change the model. For example,
we show how after training just one linear layer, we can use a basic ResNet
to retrieve images using text, diagnose distribution shifts w.r.t. human
concepts, and even perform zero-shot classification nearly on par with
CLIP.

*Speaker Bio:* Mazda Moayeri is a third year PhD student in the Computer
Science Department at the University of Maryland, advised by Dr. Soheil
Feizi. His research focuses on building practical, efficient methods to
improve the reliability and trustworthiness of AI. Having worked on
adversarial and distributional robustness, his work now combines
interpretability with robustness to diagnose distribution shifts and tailor
mitigation strategies specific to the uncovered vulnerabilities. He is
supported by the ARCS foundation as their Endowment Scholar and will be
hosted by FAIR in the summer.

*In person: *GHC 6115
*Zoom Link*:
https://cmu.zoom.us/j/99510233317?pwd=ZGx4aExNZ1FNaGY4SHI3Qlh0YjNWUT09

Thanks,
Asher Trockman
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