Learning feed-forward one-shot learners
Authors: Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, Andrea Vedaldi
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate learnets against baseline one-shot architectures (sect. 3.1) on two one-shot learning problems in Optical Character Recognition (OCR; sect. 3.2) and visual object tracking (sect. 3.3). All experiments were performed using Mat Conv Net [22]. |
| Researcher Affiliation | Academia | Luca Bertinetto University of Oxford luca@robots.ox.ac.uk, João F. Henriques University of Oxford joao@robots.ox.ac.uk, Jack Valmadre University of Oxford jvlmdr@robots.ox.ac.uk, Philip H. S. Torr University of Oxford philip.torr@eng.ox.ac.uk, Andrea Vedaldi University of Oxford vedaldi@robots.ox.ac.uk |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | Yes | For this, we use the Omniglot dataset [13], which contains images of handwritten characters from 50 different alphabets. |
| Dataset Splits | Yes | In this expression, the performance of the predictor extracted by the learnet from the exemplar zi is assessed on a single validation pair (xi, ℓi) |
| Hardware Specification | No | The paper mentions 'on a GPU' when discussing a related work (SO-DLT), but does not provide specific hardware details (like GPU models, CPU types, or memory) used for its own experiments. |
| Software Dependencies | No | The paper states 'All experiments were performed using Mat Conv Net [22]', but does not provide a specific version number for this software or any other dependencies. |
| Experiment Setup | Yes | The baseline stream ϕ for the siamese, siamese learnet, and single-stream learnet architecture consists of 3 convolutional layers, with 2 2 max-pooling layers of stride 2 between them. The filter sizes are 5 5 1 16, 5 5 16 64 and 4 4 64 512. ... All networks are trained from scratch using SGD for 50 epoch of 50,000 sample triplets (zi, xi, ℓi). The same hyperparameters (learning rate of 10 2 geometrically decaying to 10 5, weight decay of 0.005, and small mini-batches of size 8) are used for all experiments... |