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Found 8 papers, 4 papers with code
EAGLES: Efficient Accelerated 3D Gaussians with Lightweight EncodingS
We validate the effectiveness of our approach on a variety of datasets and scenes preserving the visual quality while consuming 10-20x less memory and faster training/inference speed.
SHACIRA: Scalable HAsh-grid Compression for Implicit Neural Representations
Implicit Neural Representations (INR) or neural fields have emerged as a popular framework to encode multimedia signals such as images and radiance fields while retaining high-quality.
NIRVANA: Neural Implicit Representations of Videos with Adaptive Networks and Autoregressive Patch-wise Modeling
This design shares computation within each group, in the spatial and temporal dimensions, resulting in reduced encoding time of the video.
LilNetX: Lightweight Networks with EXtreme Model Compression and Structured Sparsification
We introduce LilNetX, an end-to-end trainable technique for neural networks that enables learning models with specified accuracy-rate-computation trade-off.
One Network Doesn't Rule Them All: Moving Beyond Handcrafted Architectures in Self-Supervised Learning
We conduct a large-scale study with over 100 variants of ResNet and MobileNet architectures and evaluate them across 11 downstream scenarios in the SSL setting.
Towards Discovery and Attribution of Open-world GAN Generated Images
Through extensive experiments, we show that our algorithm discovers unseen GANs with high accuracy and also generalizes to GANs trained on unseen real datasets.
The Lottery Ticket Hypothesis for Object Recognition
The recently proposed Lottery Ticket Hypothesis (LTH) states that deep neural networks trained on large datasets contain smaller subnetworks that achieve on par performance as the dense networks.
A Unified Learning Based Framework for Light Field Reconstruction from Coded Projections
Here, we present a unified learning framework that can reconstruct LF from a variety of multiplexing schemes with minimal number of coded images as input.
