Rakshith Subramanyam

[Kowshik Thopalli @kowshik0808]

Need to adapt classifiers to novel target domains but don't have enough data? We are excited to present SiSTA, which adapts generative models with only one target sample and produces target-aware augmentations!. Poster#104@10:30 today #ICML2023 #icml w/ amazing co-authors. A 🧵

[Rakshith Subramanyam @rakshith_subra]

Venturing into single-shot domain adaptation? Meet SiSTA, our solution for large distribution shifts using a single-shot target! 🎯
🌐 Project: https://t.co/cS5cna4NZ3
Join us: ⏱️ Session: 27 Jul, 10:30 a.m. HST (Poster #104)
In collaboration with @kowshik0808 @pturaga1 @jjayram7

[Puja Trivedi @puja_computes]

Interested in adapting high quality, pretrained models for safe & effective generalization on downstream tasks? Check out our new paper where we take a closer at model adaptation using feature distortion & simplicity bias! https://t.co/oLJ1397MAh #ICLR2023🧵

[Jay Thiagarajan @jjayaram7]

Are large-scale generative models (StyleGAN-XL) useful for data constrained domain adaptation? Our #ICML2023 paper introduces SiSTA, a new data augmentation method that works even with one shot!! Preprint & codes coming out soon. Stay tuned. @kowshik0808 @rakshith_subra @pturaga1

[The Luminosity Lab @LuminosityLab]

🇮🇳 🇺🇸 His Excellency @SandhuTaranjitS, India's Ambassador to the United States, recently visited the Luminosity Lab to meet the students and explore the Rodel, our cutting-edge simulation designed to assist policymakers in making informed decisions about Arizona and beyond.

[Jay Thiagarajan @jjayaram7]

Codes for our #Neurips2022 paper on Delta-UQ are officially out now! If you are looking to quickly integrate an epistemic uncertainty estimator to your deep model, please check this out! @LLNL

Repo: https://t.co/muW2vxFST2
Paper: https://t.co/Vs9zsi3Dev

[Jay Thiagarajan @jjayaram7]

2. Interested in few-shot learners that generalize across domains or to even unseen datasets? Check out CAML - it uses contrastively trained knowledge graph bridges for meta learning. Here is our paper led by @rakshith_subra
https://t.co/az5om9yGhh
Jan 5 1815--1915 in Session 6B

[Puja Trivedi @puja_computes]

Come check out our work this Saturday at the PODs workshop at #ICML! We look at adapting pretrained models for both safety and generalization. :)

[Rakshith Subramanyam @rakshith_subra]

Excited to present our work on repurposing pre-trained StyleGANs to solve inverse problems for Out-Of-Distribution images at #ICML2022, #GANinversion. Check our paper SPHInX https://t.co/P3tezgCtAn
This work is in collaboration with @VivekSivaraman1 and @jjayaram7

[Jay Thiagarajan @jjayaram7]

Are you interested in re-purposing pre-trained face image StyleGANs to solve inverse problems with your own dataset? Check our paper on SPHINX, a new GAN inversion technique.
Spotlight: Tue 19 Jul 8:35 a.m. — 8:40 a.m (PDT)
Poster: Tue 19 Jul 3:30 p.m. — 5:30 p.m. (PDT)
2/9