63 citations · 231 across the 30 of their papers we have counts for
6 papers · 1 filter
LQF: Linear Quadratic Fine-Tuning
Alessandro Achille, Aditya Golatkar, Avinash Ravichandran +2
Classifiers that are linear in their parameters, and trained by optimizing a convex loss function, have predictable behavior with respect to changes in the training data, initial c…
Mixed-Privacy Forgetting in Deep Networks
Aditya Golatkar, Alessandro Achille, Avinash Ravichandran +2
We show that the influence of a subset of the training samples can be removed -- or "forgotten" -- from the weights of a network trained on large-scale image classification tasks,…
Predicting Training Time Without Training
Luca Zancato, Alessandro Achille, Avinash Ravichandran +2
We tackle the problem of predicting the number of optimization steps that a pre-trained deep network needs to converge to a given value of the loss function. To do so, we leverage…
Rethinking the Hyperparameters for Fine-tuning
Hao Li, Pratik Chaudhari, Hao Yang +4
Fine-tuning from pre-trained ImageNet models has become the de-facto standard for various computer vision tasks. Current practices for fine-tuning typically involve selecting an ad…
Multi-Task Incremental Learning for Object Detection
Xialei Liu, Hao Yang, Avinash Ravichandran +2
Multi-task learns multiple tasks, while sharing knowledge and computation among them. However, it suffers from catastrophic forgetting of previous knowledge when learned incrementa…
Incremental Meta-Learning via Indirect Discriminant Alignment
Qing Liu, Orchid Majumder, Alessandro Achille +3
Majority of the modern meta-learning methods for few-shot classification tasks operate in two phases: a meta-training phase where the meta-learner learns a generic representation b…