Learning feed-forward one-shot learners
Authors: Luca Bertinetto, João F. Henriques, Jack Valmadre, Philip Torr, Andrea Vedaldi
NeurIPS 2016 | Venue 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 EMAIL, João F. Henriques University of Oxford EMAIL, Jack Valmadre University of Oxford EMAIL, Philip H. S. Torr University of Oxford EMAIL, Andrea Vedaldi University of Oxford EMAIL |
| 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... |