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Lee et al. propose a regularizer to increase the size of linear regions of rectified deep networks around training and test points. Specifically, they assume piece-wise linear networks, in its most simplistic form consisting of linear layers (fully connected layers, convolutional layers) and ReLU activation functions. In these networks, linear regions are determined by activation patterns, i.e., a pattern indicating which neurons have value greater than zero. Then, the goal is to compute, and later to increase, the size $\epsilon$ such that the $L_p$-ball of radius $\epsilon$ around a sample $x$, denoted $B_{\epsilon,p}(x)$ is contained within one linear region (corresponding to one activation pattern). Formally, letting $S(x)$ denote the set of feasible inputs $x$ for a given activation pattern, the task is to determine $\hat{\epsilon}_{x,p} = \max_{\epsilon \geq 0, B_{\epsilon,p}(x) \subset S(x)} \epsilon$. For $p = 1, 2, \infty$, the authors show how $\hat{\epsilon}_{x,p}$ can be computed efficiently. For $p = 2$, for example, it results in $\hat{\epsilon}_{x,p} = \min_{(i,j) \in I} \frac{|z_j^i|}{\|\nabla_x z_j^i\|_2}$. Here, $z_j^i$ corresponds to the $j$th neuron in the $i$th layer of a multi-layer perceptron with ReLU activations; and $I$ contains all the indices of hidden neurons. This analytical form can then used to add a regularizer to encourage the network to learn larger linear regions: $\min_\theta \sum_{(x,y) \in D} \left[\mathcal{L}(f_\theta(x), y) - \lambda \min_{(i,j) \in I} \frac{|z_j^i|}{\|\nabla_x z_j^i\|_2}\right]$ where $f_\theta$ is the neural network with paramters $\theta$. In the remainder of the paper, the authors propose a relaxed version of this training procedure that resembles a max-margin formulation and discuss efficient computation of the involved derivatives $\nabla_x z_j^i$ without too many additional forward/backward passes. https://i.imgur.com/jSc9zbw.jpg Figure 1: Visualization of locally linear regions for three different models on toy 2D data. On toy data and datasets such as MNIST and CalTech-256, it is shown that the training procedure is effective in the sense that larger linear regions around training and test points are learned. For example, on a 2D toy dataset, Figure 1 visualizes the linear regions for the optimal regularizer as well as the proposed relaxed version. Also find this summary at [davidstutz.de](https://davidstutz.de/category/reading/). ![]() |
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This is an interestingly pragmatic paper that makes a super simple observation. Often, we may want a usable network with fewer parameters, to make our network more easily usable on small devices. It's been observed (by these same authors, in fact), that pruned networks can achieve comparable weights to their fully trained counterparts if you rewind and retrain from early in the training process, to compensate for the loss of the (not ultimately important) pruned weights. This observation has been dubbed the "Lottery Ticket Hypothesis", after the idea that there's some small effective subnetwork you can find if you sample enough networks. Given these two facts - the usefulness of pruning, and the success of weight rewinding - the authors explore the effectiveness of various ways to train after pruning. Current standard practice is to prune low-magnitude weights, and then continue training remaining weights from values they had at pruning time, keeping the final learning rate of the network constant. The authors find that: 1. Weight rewinding, where you rewind weights to *near* their starting value, and then retrain using the learning rates of early in training, outperforms fine tuning from the place weights were when you pruned but, also 2. Learning rate rewinding, where you keep weights as they are, but rewind learning rates to what they were early in training, are actually the most effective for a given amount of training time/search cost To me, this feels a little bit like burying the lede: the takeaway seems to be that when you prune, it's beneficial to make your network more "elastic" (in the metaphor-to-neuroscience sense) so it can more effectively learn to compensate for the removed neurons. So, what was really valuable in weight rewinding was the ability to "heat up" learning on a smaller set of weights, so they could adapt more quickly. And the fact that learning rate rewinding works better than weight rewinding suggests that there is value in the learned weights after all, that value is just outstripped by the benefit of rolling back to old learning rates. All in all, not a super radical conclusion, but a useful and practical one to have so clearly laid out in a paper. ![]() |
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This paper builds on top of a bunch of existing ideas for building neural conversational agents so as to control against generic and repetitive responses. Their model is the sequence-to-sequence model with attention (Bahdanau et al.), first trained with the usual MLE loss and fine-tuned with policy gradients to optimize for specific conversational properties. Specifically, they define 3 rewards: 1. Ease of answering — Measured as the likelihood of responding to a query with a list of hand-picked dull responses (more negative log likelihood is higher reward). 2. Information flow — Consecutive responses from the same agent (person) should have different information, measured as negative of log cosine distance (more negative is better). 3. Semantic coherence — Mutual information between source and target (the response should make sense wrt query). $P(a|q) + P(q|a)$ where a is answer, q is question. The model is pre-trained with the usual supervised objective function, taking source as concatenation of two previous utterances. Then they have two stages of policy gradient training, first with just a mutual information reward and then with a combination of all three. The policy network (sequence-to-sequence model) produces a probability distribution over actions (responses) given state (previous utterances). To estimate the gradient in an iteration, the network is frozen and responses are sampled from the model, the rewards for which are then averaged and gradients are computed for first L tokens of response using MLE and remaining T-L tokens with policy gradients, with L being gradually annealed to zero (moving towards just the long-term reward). Evaluation is done based on length of dialogue, diversity (distinct unigram, bigrams) and human studies on 1. Which of two outputs has better quality (single turn) 2. Which of two outputs is easier to respond to, and 3. Which of two conversations have better quality (multi turn). ## Strengths - Interesting results - Avoids generic responses - 'Ease of responding' reward encourages responses to be question-like - Adding in hand-engineereed approximate reward functions based on conversational properties and using those to fine-tune a pre-trained network using policy gradients is neat. - Policy gradient training also encourages two dialogue agents to interact with each other and explore the complete action space (space of responses), which seems desirable to identify modes of the distribution and not converge on a single, high-scoring, generic response. ## Weaknesses / Notes - Evaluating conversational agents is hard. BLEU / perplexity are intentionally avoided as they don't necessarily reward desirable conversational properties. ![]() |
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# Simultaneous Deep transfer across domains and tasks ## Tzeng, Hoffman, Saenko, 2015 * The paper aims to exploit unlabeled and sparsely labeled data from the target domain. * As a baseline, they mention that one could match feature distributions between source and target domain. This work will also explore correlation between categories, such as _bottle_ and _mug._ * The authors derive inspiration from the _Name the dataset_ game by Torralbe and Efros. In this game, you train a classifier to predict which dataset an image originates from. This idea transpires into the domain confusion loss. The domain classifier measures the confusion between learned features from source and target domain. The image classifier learns a feature representation that makes the domain inditinguishable, as measured by the domain confusion. * The second idea also learns the similarity structure between objects in the target domain. This works as follows. _We first compute the average output probability distribution, or “softlabel,” over the source training examples in each category. Then, for each target labeled example, we directly optimize our model to match the distribution over classes to the soft label. In this way we are able to perform task adaptation by transferring information to categories with no explicit labels in the target domain._ * The experiments take place in two situations. The _supervised_ case, where only few labels are present in the target domain. The _semi supervised_ case, where only few labels of a subset of the classes are present. * In the final section, the authors perform analysis on theis own result. They show how the image classifier correctly labeled monitor, while no labels for monitor were present in the target domain. ![]() |
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This paper tackles a challenging task of hand shape and continuous Sign Language Recognition (SLR) directly from images obtained from a common RGB camera (rather than utilizing motion sensors like Kinect). The basic idea is to create a network that is end-to-end trainable with input (i.e. images) and output (i.e. hand shape labels, word labels) sequences. The network is composed of three parts: - CNN as a feature extractor - Bidirectional LSTMs for temporal modeling - Connectionist Temporal Classification as a loss layer ![Network structure](https://ai2-s2-public.s3.amazonaws.com/figures/2017-08-08/3269d3541f0eec006aee6ce086db2665b7ded92d/1-Figure1-1.png) Results: - Observed state-of-art results (at the time of publishing) on "One-Million Hands" and "RWTH-PHOENIX-Weather-2014" datasets. - Utilizing full images rather than hand patches provides better performance for continuous SLR. - A network that recognizes hand shape and a network that recognizes word sequence can be combined and trained together to recognize word sequences. Finetuning combined system from for all layers works better than fixing "feature extraction" layers. - Combination of two networks where each network trained on separate task performs slightly better than training each network on word sequences. - Marginal difference in performance observed for different decoding and post-processing techniques during sequence-to-sequence predictions. ![]() |