150 points by ai_researcher 1 year ago flag hide 12 comments
john_doe 1 year ago next
This is fascinating! The paper's theory about using auxiliary networks for loss regularization appears to be very promising. I can't wait to see how this will impact the field. (https://arxiv.org/abs/XXXXX)
alice_wonderland 1 year ago next
Absolutely, I am also looking forward to the practicality and scalability of this method in real-world applications, especially for high-dimensional datasets.
deep_learning_fan 1 year ago prev next
Very cool! Any references for related publications that tackle related problems? I'm curious since I'm doing research in that direction as well.
john_doe 1 year ago next
@deep_learning_fan, here are a few relevant papers that use a similar concept: [1] Auxiliary Autoencoders for Domain Adaptation, [2] Hierarchical Auxiliary Loss for Scene Segmentation, and [3] Unsupervised Deep Learning of Shape Abstractions using Auto-Encoded Variational Bayes.
ml_master 1 year ago prev next
From the blog post, it's not clear how well this method scales for large-scale datasets. Can someone share their experience using this for Imagenet or other large datasets?
bigdata_champ 1 year ago next
@ml_master, I've actually been experimenting with this method in large-scale datasets. It doesn't seem to have a major impact on the GPU and memory usage since the auxiliary networks are smaller compared to the main network. It has some overhead, but overall, it scales better than expected.
critical_thinker 1 year ago prev next
The theory describes some really interesting applications in NLP tasks. Would the impact be significant, or would other recent models yield more improvements?
language_model 1 year ago next
@critical_thinker, that's an excellent question. The approach may not yield a massive improvement in NLP tasks individually, but the cumulative impacts on numerous tasks could lead to a substantial overall improvement. It's worth further exploration.
hyperparam_hero 1 year ago prev next
When using the auxiliary networks, did the researchers perform any hyperparameter tuning with respect to the number of auxiliary networks, network topologies, or learning rates?
john_doe 1 year ago next
@hyperparam_hero, Yes, they touched on the subject in the appendix but admitted that more extensive hyperparameter tuning could lead to even better results. Topologies ranged from simple feedforward networks to convolutional and recurrent layers.
datapoint 1 year ago prev next
In the blog post a statement was made comparing their results to methods that used transfer learning and pre-training. Did they consider possible design biases leading to the superior performance of their networks?
skeptic_nerd 1 year ago next
@datapoint, in the paper, they mentioned an independent researcher performed a reproducibility test and confirmed the results. I’m assuming bias could be checked during this test, but perhaps that’s for a follow-up paper. What do you think?