rahafaljundi Goto Github PK

automatic_actor_labelling

The code for the ACCV 2016 paper: Who's that Actor? Automatic Labelling of Actors in TV series starting from IMDB Images

editable

encoder-based-lifelong-learning

examples

A set of examples around pytorch in Vision, Text, Reinforcement Learning, etc.

expert-gate

We introduce a model of lifelong learning, based on a Network of Experts. New tasks / experts are learned and added to the model sequentially, building on what was learned before. To ensure scalability of this process, data from previous tasks cannot be stored and hence is not available when learning a new task. A critical issue in such context, not addressed in the literature so far, relates to the decision of which expert to deploy at test time. We introduce a set of gating autoencoders that learn a representation for the task at hand, and, at test time, automatically forward the test sample to the relevant expert. This also brings memory efficiency as only one expert network has to be loaded into memory at any given time. Further, the autoencoders inherently capture the relatedness of one task to another, based on which the most relevant prior model to be used for training a new expert, with finetuning or learning without-forgetting, can be selected. We evaluate our method on image classification and video prediction problems.

gradient-based-sample-selection

mas-memory-aware-synapses

Memory Aware Synapses method implementation code

maximally_interfered_retrieval

Codebase for "Online Continual Learning with Maximally Interfered Retrieval"

pytorch-implementation-of-encoder-based-lifelong-learning

selfless-sequential-learning

selfless-sequential-learning-deprecated-

task_free_continual_learning

This repository demonstrates the application of our proposed task-free continual learning method on a synthetic experiment.

yourroad

university