Search Results for author: Michael Lam

Found 6 papers, 3 papers with code

Rethinking the Hyperparameters for Fine-tuning

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.

Transfer Learning

Toward Understanding Catastrophic Forgetting in Continual Learning

no code implementations2 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.

Continual Learning

Task2Vec: Task Embedding for Meta-Learning

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.


Unsupervised Video Summarization With Adversarial LSTM Networks

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.

Unsupervised Video Summarization

Fine-Grained Recognition as HSnet Search for Informative Image Parts

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).

Fine-Grained Image Classification Informativeness

HC-Search for Structured Prediction in Computer Vision

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.

Monocular Depth Estimation object-detection +3

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