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pro vyhledávání: '"Rojas-Gomez, Renan"'
Autor:
Morningstar, Warren, Bijamov, Alex, Duvarney, Chris, Friedman, Luke, Kalibhat, Neha, Liu, Luyang, Mansfield, Philip, Rojas-Gomez, Renan, Singhal, Karan, Green, Bradley, Prakash, Sushant
We study the relative effects of data augmentations, pretraining algorithms, and model architectures in Self-Supervised Learning (SSL). While the recent literature in this space leaves the impression that the pretraining algorithm is of critical impo
Externí odkaz:
http://arxiv.org/abs/2403.05726
Autor:
Rojas-Gomez, Renan A., Singhal, Karan, Etemad, Ali, Bijamov, Alex, Morningstar, Warren R., Mansfield, Philip Andrew
Existing data augmentation in self-supervised learning, while diverse, fails to preserve the inherent structure of natural images. This results in distorted augmented samples with compromised semantic information, ultimately impacting downstream perf
Externí odkaz:
http://arxiv.org/abs/2312.01187
For computer vision tasks, Vision Transformers (ViTs) have become one of the go-to deep net architectures. Despite being inspired by Convolutional Neural Networks (CNNs), ViTs remain sensitive to small shifts in the input image. To address this, we i
Externí odkaz:
http://arxiv.org/abs/2305.16316
We propose learnable polyphase sampling (LPS), a pair of learnable down/upsampling layers that enable truly shift-invariant and equivariant convolutional networks. LPS can be trained end-to-end from data and generalizes existing handcrafted downsampl
Externí odkaz:
http://arxiv.org/abs/2210.08001
Despite unconditional feature inversion being the foundation of many image synthesis applications, training an inverter demands a high computational budget, large decoding capacity and imposing conditions such as autoregressive priors. To address the
Externí odkaz:
http://arxiv.org/abs/2106.06927
Seismic full-waveform inversion (FWI) is a nonlinear computational imaging technique that can provide detailed estimates of subsurface geophysical properties. Solving the FWI problem can be challenging due to its ill-posedness and high computational
Externí odkaz:
http://arxiv.org/abs/2009.01807
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