## 3 Learning-Based Action Representation Approach
### Deep Learning-Based Approaches
#### 3.2.3 Discussion  
In this section we summarize and discuss the learning-based action representation approaches. These approaches have been divided into genetic programming, dictionary learning and supervised and unsupervised deep learning-based approaches according to the learning representation used in each category. However, this division boundary is not strict and approaches may overlap.  
The dictionary learning-based approaches have attracted increasing interest of researchers in computer vision, specifically in human activity recognition. These approaches have introduced the concept of unified learning of dictionary and corresponding classifier into a single learning procedure, which leads to the concept of end-to-end learning. On the other hand, genetic programming (GP) is a powerful evolutionary method inspired by natural selection, used to solve the problem without prior domain knowledge. In human action recognition, GP is used to design the holistic descriptors that are adaptive, and robust for action recognition. These methods have achieved state of the art results on challenging action recognition datasets.  
Deep learning has emerged as highly popular direction within the machine learning which has outperformed the traditional approaches in many applications of computer vision. The highly advantageous property of deep learning algorithms is their ability to learn features from the raw data, which eliminates the need of handcrafted feature detectors and descriptors. There are two categories of deep learning models, i.e., unsupervised/generative and supervised/discriminative models. The DBN is a popular generative model which has been used for human action recognition. This model has already achieved high performance on challenging datasets as compared to its traditional handcrafted counterparts [139]. On the other hand, CNN is one of the most popular deep learning models in the supervised learning category. Most of the existing learning-based representations either directly apply CNN to video frames or variations of CNN for spatio-temporal features. These models have also achieved excellent results on challenging human activity recognition datasets as recoded in Table 3. So far, supervised deep learning models have achieved better performance but some studies suggest that unsupervised learning is going to be far more important in the long run. Since we discover the world by observing it rather being told the name of every object, human and animal learning is mostly unsupervised [13].  
The deep learning models have also some limitations: These models require huge amount of data for training the algorithm. Most of the action recognition datasets such as KTH [35], IXMAS [118], HDMB-51 [47], and UCF Sports [43; 44] are comparatively small for training these models. However, recently a large-scale ActivityNet dataset [176] was proposed with 200 action categories, 849 hours of video in total. This dataset is suitable to train the deep learning-based algorithms. We can expect a major breakthrough with development of algorithms that could produce remarkable results on this dataset.