1 code implementation • ICLR 2020 • Hao Li, Pratik Chaudhari, Hao Yang, Michael Lam, Avinash Ravichandran, Rahul Bhotika, Stefano Soatto
Our findings challenge common practices of fine-tuning and encourages deep learning practitioners to rethink the hyperparameters for fine-tuning.
no code implementations • 2 Aug 2019 • Cuong V. Nguyen, Alessandro Achille, Michael Lam, Tal Hassner, Vijay Mahadevan, Stefano Soatto
As an application, we apply our procedure to study two properties of a task sequence: (1) total complexity and (2) sequential heterogeneity.
1 code implementation • ICCV 2019 • Alessandro Achille, Michael Lam, Rahul Tewari, Avinash Ravichandran, Subhransu Maji, Charless Fowlkes, Stefano Soatto, Pietro Perona
We demonstrate that this embedding is capable of predicting task similarities that match our intuition about semantic and taxonomic relations between different visual tasks (e. g., tasks based on classifying different types of plants are similar) We also demonstrate the practical value of this framework for the meta-task of selecting a pre-trained feature extractor for a new task.
1 code implementation • CVPR 2017 • Behrooz Mahasseni, Michael Lam, Sinisa Todorovic
The summarizer is the autoencoder long short-term memory network (LSTM) aimed at, first, selecting video frames, and then decoding the obtained summarization for reconstructing the input video.
no code implementations • CVPR 2017 • Michael Lam, Behrooz Mahasseni, Sinisa Todorovic
This motivates us to formulate our problem as a sequential search for informative parts over a deep feature map produced by a deep Convolutional Neural Network (CNN).
no code implementations • CVPR 2015 • Michael Lam, Janardhan Rao Doppa, Sinisa Todorovic, Thomas G. Dietterich
The mainstream approach to structured prediction problems in computer vision is to learn an energy function such that the solution minimizes that function.