Search Results for author:
Found 7 papers, 5 papers with code
Humans Beat Deep Networks at Recognizing Objects in Unusual Poses, Given Enough Time
Deep learning is closing the gap with humans on several object recognition benchmarks.
ViewFusion: Learning Composable Diffusion Models for Novel View Synthesis
Deep learning is providing a wealth of new approaches to the old problem of novel view synthesis, from Neural Radiance Field (NeRF) based approaches to end-to-end style architectures.
On the special role of class-selective neurons in early training
Finally, in causal experiments where we regularize against class selectivity at different points in training, we show that the presence of class-selective neurons early in training is critical to the successful training of the network; in contrast, class-selective neurons can be suppressed later in training with little effect on final accuracy.
Blockwise Self-Supervised Learning at Scale
Current state-of-the-art deep networks are all powered by backpropagation.
Progress and limitations of deep networks to recognize objects in unusual poses
We show that classifying these images is still a challenge for all networks tested, with an average accuracy drop of 29. 5% compared to when the objects are presented upright.
Barlow Twins: Self-Supervised Learning via Redundancy Reduction
This causes the embedding vectors of distorted versions of a sample to be similar, while minimizing the redundancy between the components of these vectors.
Ranked #11 on
Image Classification
on Places205
Addressing the Topological Defects of Disentanglement via Distributed Operators
A core challenge in Machine Learning is to learn to disentangle natural factors of variation in data (e. g. object shape vs. pose).
