I like the idea proposed in this paper - training on a label-rich domain and transfer the representation to a label-limited domain, but would like to extend it to data more than faces such as transferring the object attributes.

Joint summary from https://mitpress.mit.edu/books/developmental-robotics

Developmental robotics is the interdisciplinary approach to the autonomous design of behavioural and cognitive capabilities in artificial agents (robots) that takes direct inspiration from the developmental principles and mechanisms observed in the natural cognitive systems.  It relies on a highly interdisciplinary effort of empirical developmental sciences such as developmental psychology, neuroscience, and comparative psychology, and computational and engineering disciplines such as robotics and artificial intelligence.  The implementation of these principles and mechanisms into a robot-s control architecture and the testing through experiments where the robot interacts with its physical and social environment simultaneously permits the validation of such principles and the actual design of complex behavioural and mental capabilities in robots. Developmental psychology and developmental robotics mutually benefit from such a combined effort.

Synonym terms of Developmental Robotics include cognitive developmental robotics, autonomous mental development as well as epigenetic robotics \cite{Cangelosi18}. The  later term borrows the term ‘epigenetic’ from Piaget’s Epigenetic Theory of human development, where the child’s cognitive system develops as a result of the interaction between genetic predispositions and the organism’s interaction with the environment.  Therefore, ‘Epigenetic robotics’ was justified by Piaget’s stress on the importance of the role of interaction with the environment (later complemented with the emphasis on social interaction of Zlatev and Balkenius in 2001), and in particular, on the sensorimotor bases of higher-order cognitive capabilities. 
Due to the challenges of robotics domain not only on acquisition of skills in an incremental manner but also in open-ended environments, it is perhaps worth noticing that the state of the art is not as advanced as in other more constrained well defined unique task settings. Because of the importance of embodiment in robotics for open ended learning \cite{Cangelosi18} and because of the scarcity of continual learning strategies on robotics and merely assessed  on perception tasks, we believe both fields methodologies should borrow from each other. The blend could result not only in more effective or robust lifelong learning but also in less forgetful agents (i.e., the main rationale behind CL), which has not yet empirically shown is impossible to reach. We hope this survey helps reach this goal.

Dynamical systems development: In math it is characterized by complex changes over time in the phase state. The complex interaction of nonlinear phenomena results in the production of unpredictable states of the system, often referred to as emergent states. This concept was borrowed by developmental psychologists \cite{Thelen94} to explain child development as the emergent product of the intricate and dynamic interaction of many decentralized and local interactions related to the child growing body and brain and her environment.


Some definitions: 

Novelty, curiosity and surprise: focuses on the autonomy of learning, i.e. on the agents freedom to choose what, when and how it will learn. Intrinsic motivation (IM) is a mechanism to drive autonomous learning, not only in developmental robotics but also more broadly within the field of machine learning \cite{Mirolli13}\cite{Oudeyer07}. IM is task-independent, the agent could be placed in a completely new environment with no prior knowledge  or experience, and through self-directed exploration the robot will potentially learn not only important features of the environment but also the behavioural skills necessary for dealing with the environment. Second, IM promotes the hierarchical learning rather than solving a specific, predefined task. 

The Theory of MInd (ToM): A robot can use its own theory of mind to improve the interaction with human users, for example. It aims at reading the intention of the others and reacting appropriately to the emotional, attentional and cognitive states of the other agents, to anticipate their reactions, and to modify its own behaviour to satisfy these expectation and needs \cite{Scasellati02}.

Cascades: developmental theories refer to the far reach of early developments on later ones in terms of the “developmental cascade”.

Saliency in robotics: 
In robotics vision, a set of feature extraction methods typically can be applied to derive a saliency map \cite{Itti01}, i.e. the identification of the parts of the image that are important for the robot behaviour. It can be created, e.g., by combining a set of feature extraction method for color, motion, orientation and brightness. These features are combined to generate the whole saliency  map used by the model to fixate the objects.

Bigtable is the distributed, scalable, highly available and high performing database that powers multiple applications across Google — each having a different demand in terms of the size of data to be stored and latency with which results are expected. Though Bigtable is not open source itself, the paper was crucial in the development of powerful open source databases like Cassandra (borrows BigTable’s data model), HBase and LevelDB. The paper Bigtable: A Distributed Storage System for Structured Data describes the story of how BigTable is designed and how it works.

#### Data Model
Bigtable is a sorted, multidimensional key-value store indexed by a row key, a column key, and a timestamp. For example in the context of storing web pages, the page URL could be the row key, various attributes of the page could be the column key and the time when the page was fetched could be the timestamp. Rows are maintained in lexicographic order and row range is dynamically partitioned with each row range being referred to as a tablet. Reads over short range are very fast due to locality and all read/write operations under a row are atomic. Column keys are grouped into sets called column family. These sets are created before any data is stored. A table should have a small number of distinct column families. A column key has the following syntax — family:qualifier. For example, for family as language, the keys could be language:english or language:hindi. A row in a Bigtable can contain multiple versions of same data, indexed by timestamps, and stored in decreasing order of timestamps. Old versions can be garbage collected in the way that the client can specify that only last n entries are to be kept or entries for only last n days(hours/weeks etc) are to be kept.

#### Building Blocks
Bigtable uses Google File System (GFS) for storing logs and data in SSTable file format. SSTable provides an immutable, ordered (key, value) map. Immutability provides certain advantages which will be discussed later. An SSTable consists of a sequence of blocks and a block index to locate the blocks. When the SSTable is opened, this index is loaded into the main memory for fast lookup.
Bigtable also uses a highly available and persistent distributed lock service called Chubby for handling synchronization issues. Chubby provides a namespace consisting of directories and small files which can be used as locks. Read and write access to a file is atomic. Clients maintain sessions with a Chubby service which needs to be renewed regularly. Bigtable depends on Chubby so much that if Chubby is unavailable for an extended period of time, Bigtable will also be unavailable.

#### Implementation

There are 3 major components — a library linked into the client, one master server and multiple tablet servers. Master server assigns tablets to tablet servers, load balances these servers and garbage collect files in GFS. But the client data does not move through the master, clients directly contact tablet servers for read and write operations. Tablet servers manage a set of tablets — they handle the read/write operations directed to these tablets and also split very large tablets.

A 3-level hierarchy is maintained to store tablet location information. The first level is a file stored in Chubby that has the location of the root table. Root table contains the location of all tables in a METADATA tablet. Each entry in this METADATA tablet contains the location of a set of user tablets. These tablet locations are cached in the client library and can also be fetched from the above scheme by recursively moving up the hierarchy.

Each tablet is assigned to one tablet server at a time. The master server keeps track of which servers are alive, which tablet is assigned to which server and which tablets are unassigned. When a tablet server restarts, it creates and acquires an exclusive lock on a unique file in the servers directory (Chubby directory) which is monitored by the master server. If the tablet server loses its exclusive lock, it will stop serving its tablets though it will attempt to reconnect as long as the file exists in the servers directory. In case the file is deleted, the tablet server kills itself. The master tracks tablets that are not being served and reassigns them. To avoid network partition issues, the master kills itself if its Chubby session expires though its tablets are not reassigned.

A master performs following tasks at startup time:

1. Grab the unique master lock to prevent concurrent master instantiation.
2. Find live servers by scanning the servers directory.
3. Find what tablets are assigned to each server by messaging them.
4. Find the set of all tablets by scanning the METADATA tablet.

Tablet creation, deletion or merge is initiated by the master server while tablet partition is handled by tablet servers who notifies the master. In case the notification is lost, the master would be notified when it asks for the split tablet to be loaded.

Memtables are in-memory buffers to store recently committed updates to Bigtable. These updates are later written to GFS for persistent storage. Tablets can be recovered by reading the metadata from METADATA tablet which contains a set of Redo points and list of SSTables comprising the tablet.

There are 3 kinds of compactions in action:

1. Minor compaction — As the memtable grows in size and reaches a certain threshold, it is frozen, a new memtable is created and the frozen memtable is written to GFS.
2. Merging Compaction — Minor compaction keeps increasing the count of SSTables. Merging compaction reads the contents of a few SSTables and the memtable and writes it to a new SSTable.
3. Major Compaction — In Major compaction, all the SSTables are written into a single SSTable.

#### Refinements
Along with the described implementation, several refinements are required to make the system reliable and high performing.

Clients can club together column families into locality groups for which separate SSTables are generated. This allows for more efficient reads. If SSTables are small enough, they can be kept in main memory as well.

SSTables can be compressed in various formats. The compression ratio gets even better when multiple versions of same data are stored.

**Caching** — Tablet servers employ 2 levels of caching.

1. Scan Cache — It caches (key, value) pairs returned by the SSTable and is useful for applications that read same data multiple times.
2. Block Cache — It caches SSTable blocks read from GFS and useful when the application uses locality of reference.

**BloomFilters** are created for SSTables (particularly for the locality groups). They help to reduce the number of disk access by predicting if an SSTable may contain data corresponding to a particular row, column pair.

**Commit Logs** are maintained at tablet server level instead of tablet level to keep the number of log file small. Each tablet server maintains 2 log writing threads — each writing to its own and separate log file and only one of the threads is active at a time. If one of the threads is performing poorly (say due to network congestion), the writing switches to other thread. Log entries have sequence numbers to allow recovery process.

We earlier saw that SSTable entries are immutable. The advantage is that no synchronization is needed during read operations. This also makes it easier to split tablets. Permanently removing deleted data is taken care of by garbage collector.

Kim et al. propose Concept Activation Vectors (CAV) that represent the direction of features corresponding to specific human-interpretable concepts. In particular, given a network for a classification task, a concept is defined as a set of images with that concept. A linear classifier is then trained to distinguish images with concept from random images without the concept based on a chosen feature layer. The normal of the obtained linear classification boundary corresponds to the learned Concept Activation Vector (CAV). By considering the directional derivative along this direction for a given input allows to quantify how well the input aligns with the chosen concept. This way, images can be ranked and the model’ sensitivity to particular concepts can be quantified. The idea is also illustrated in Figure 1.

https://i.imgur.com/KOqPeag.png
Figure 1: Process of constructing Concept Activation Vectors (CAVs).

Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/).

Incorporating an unsupervised language modeling objective to help train a bidirectional LSTM for sequence labeling. At the same time as training the tagger, the forward-facing LSTM is optimised to predict the next word and the backward-facing LSTM is optimised to predict the previous word. The model learns a better composition function and improves performance on NER, error detection, chunking and POS-tagging, without using additional data.

https://i.imgur.com/pXLSsAR.png