Search Results for author: Xu Ji

Found 6 papers, 4 papers with code

Predicting Unreliable Predictions by Shattering a Neural Network

1 code implementation15 Jun 2021 Xu Ji, Razvan Pascanu, Devon Hjelm, Andrea Vedaldi, Balaji Lakshminarayanan, Yoshua Bengio

For piecewise linear neural networks, given a weighting function that relates the errors of different input activation regions together, we obtain a bound on each region's generalization error that scales inversely with the density of training samples.

Image Classification

Automatic Recall Machines: Internal Replay, Continual Learning and the Brain

1 code implementation22 Jun 2020 Xu Ji, Joao Henriques, Tinne Tuytelaars, Andrea Vedaldi

Replay in neural networks involves training on sequential data with memorized samples, which counteracts forgetting of previous behavior caused by non-stationarity.

Continual Learning

There and Back Again: Revisiting Backpropagation Saliency Methods

1 code implementation CVPR 2020 Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Andrea Vedaldi

Saliency methods seek to explain the predictions of a model by producing an importance map across each input sample.

Meta-Learning

NormGrad: Finding the Pixels that Matter for Training

no code implementations19 Oct 2019 Sylvestre-Alvise Rebuffi, Ruth Fong, Xu Ji, Hakan Bilen, Andrea Vedaldi

In this paper, we are rather interested by the locations of an image that contribute to the model's training.

Meta-Learning

On the role of neurogenesis in overcoming catastrophic forgetting

no code implementations6 Nov 2018 German I. Parisi, Xu Ji, Stefan Wermter

Lifelong learning capabilities are crucial for artificial autonomous agents operating on real-world data, which is typically non-stationary and temporally correlated.

Incremental Learning

Invariant Information Clustering for Unsupervised Image Classification and Segmentation

6 code implementations ICCV 2019 Xu Ji, João F. Henriques, Andrea Vedaldi

The method is not specialised to computer vision and operates on any paired dataset samples; in our experiments we use random transforms to obtain a pair from each image.

Classification General Classification +4

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