Sequentially Generated Instance-Dependent Image Representations for Classification
Authors: Ludovic Denoyer; Matthieu Cord; Patrick Gallinari; Nicolas Thome; Gabriel Dulac-Arnold
ICLR 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the system s abilities in a series of image-based exploration and classification tasks that highlight its learned exploration and inference abilities. (010-012) and We present an experimental evaluation of this method on three different classical datasets and propose a qualitative study explaining the behaviour of this model. (265-268) |
| Researcher Affiliation | Academia | LIP6, UPMC Sorbonne University Paris, France (002-003) |
| Pseudocode | Yes | Algorithm 1 Inference Algorithm (385) and Algorithm 2 Complete Learning algorithm (440) and Algorithm 3 Classification Policy Learning Algorithm (495) and Algorithm 4 Exploration Sub-policy πγk Learn. Algorithm (550) |
| Open Source Code | No | The paper does not provide any explicit statement about open-sourcing its code or a link to a code repository. |
| Open Datasets | Yes | We evaluate the proposed method on two challenging image databases corresponding to different tasks: fine-grained image classification (People Playing Musical Instruments dataset5) (Yao & Fei-Fei, 2010) and scene recognition (15-scenes dataset) (Lazebnik et al., 2006). (615-619) and http://ai.stanford.edu/ bangpeng/ppmi.html (620) |
| Dataset Splits | Yes | We randomly sample training/testing images on 5 splits of the data, and the final performance corresponds to the average performance obtained on the 5 runs. (677-680) |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper mentions using 'VLFEAT library' but does not specify its version number or any other software dependencies with version details. |
| Experiment Setup | Yes | These local features are computed at a single scale (s = 16 pixels) and with a constant step size d. For 15-scenes we use d = 8 pixels while d = 4 pixels for PPMI. (623-625) and We run a K-Means algorithm by randomly sampling about 1 million descriptors in each database to produce a dictionnary of M = 200 codeword elements. (626-629) and We have chosen to create 10 sequences of regions for each training images, resulting in a training set of size 10 ℓfor each classifier fθ and πγt learned by our method. (643-646) |