Representation learning is concerned with building intermediate representations of data useful to solve machine learning tasks. This work falls in the area of representation/feature learning, which is an unsupervised learning problem. This performance is close to that obtained in the supervised case by AlexNet ( \(56.5\,\%\) mAP for detection and \(78.2\,\%\) in classification). Moreover, these features used as pre-training in the Fast R-CNN pipeline achieve \(51.8\,\%\) mAP for detection and \(68.6\,\%\) for classification on PASCAL VOC 2007. In object classification these features lead to the best accuracy ( \(38.1\,\%\)) when compared to other existing features trained via self-supervised learning on the ILSVRC2012 dataset. Moreover, the features are highly transferrable to detection and classification and yield the highest performance to date for an unsupervised method. Also, there is no need to handle chromatic aberration or to build robustness to pixelation. Training a jigsaw puzzle solver takes about 2.5 days compared to 4 weeks of. This argument is supported by our experimental validation. However, when all the tiles are observed, the ambiguities might be eliminated more easily because the tile placement is mutually exclusive. The association of each separate puzze tile to a precise object part might be ambiguous. ![]() We argue that solving jigsaw puzzles can be used to teach a system that an object is made of parts and what these parts are. 1), which builds features that yield high performance when transferred to detection and classification tasks. We introduce a novel self-supervised task, the jigsaw puzzle reassembly problem (see Fig. ![]() This work shows that this is indeed the case. While it is true that biological agents typically make use of multiple images and also integrate additional sensory information, such as ego-motion, it is also true that single snapshots may carry more information than we have been able to extract so far. The features obtained with these approaches have been successfully transferred to classification and detections tasks, and their performance is very encouraging when compared to features trained in a supervised manner.Ī fundamental difference between and is that the former method uses single images as the training set and the other two methods exploit multiple images related either through a temporal or a viewpoint transformation. uses object correspondence obtained through tracking in videos, and uses ego-motion information obtained by a mobile agent such as the Google car. uses the relative spatial co-location of patches in images as a label. The main idea is to exploit different labelings that are freely available besides or within visual data, and to use them as intrinsic reward signals to learn general-purpose features. ![]() have explored a novel paradigm for unsupervised learning called self-supervised learning. However, as manually labeled data can be costly, unsupervised learning methods are gaining momentum. Visual tasks, such as object classification and detection, have been successfully approached through the supervised learning paradigm, where one uses labeled data to train a parametric model. These features outperform all current unsupervised features with \(51.8\,\%\) for detection and \(68.6\,\%\) for classification, and reduce the gap with supervised learning ( \(56.5\,\%\) and \(78.2\,\%\) respectively). We pre-train the CFN on the training set of the ILSVRC2012 dataset and transfer the features on the combined training and validation set of Pascal VOC 2007 for object detection (via fast RCNN) and classification. Our experimental evaluations show that the learned features capture semantically relevant content. The later layers of the CFN then use the features to identify their geometric arrangement. The features correspond to the columns of the CFN and they process image tiles independently (i.e., free of context). To facilitate the transfer of features to other tasks, we introduce the context-free network (CFN), a siamese-ennead convolutional neural network. The pre-training consists of solving jigsaw puzzles of natural images. ![]() The features are pre-trained on a large dataset without human annotation and later transferred via fine-tuning on a different, smaller and labeled dataset. We propose a novel unsupervised learning approach to build features suitable for object detection and classification.
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